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.
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.
Soon shard pruning will be optimized not to generally work linearly
anymore. Thus we can't print the pruned shard intervals as currently
done anymore.
The current printing of shard ids also prevents us from running tests
in parallel, as otherwise shard ids aren't linearly numbered.
Pretty straightforward. Had some concerns about locking, but due to the
fact that all distributed operations use either some level of deparsing
or need to enumerate column names, they all block during any concurrent
column renames (due to the AccessExclusive lock).
In addition, I had some misgivings about permitting renames of the dis-
tribution column, but nothing bad comes from just allowing them.
Finally, I tried to trigger any sort of error using prepared statements
and could not trigger any errors not also exhibited by plain PostgreSQL
tables.
With this change we add an option to add a node without replicating all reference
tables to that node. If a node is added with this option, we mark the node as
inactive and no queries will sent to that node.
We also added two new UDFs;
- master_activate_node(host, port):
- marks node as active and replicates all reference tables to that node
- master_add_inactive_node(host, port):
- only adds node to pg_dist_node
Before this commit, we were erroring out for queries containing parameterized SQL functions
like 'SELECT parameterized_sql_query(value)' as we should, however we were returning wrong
results for queries like 'SELECT * FROM parameterized_sql_query(value)'. With this commit
we started to error out on such queries too.
In this PR, we aim to deduce whether each of the RTE_RELATION
is joined with at least on another RTE_RELATION on their partition keys. If each
RTE_RELATION follows the above rule, we can conclude that all RTE_RELATIONs are
joined on their partition keys.
In order to do that, we invented a new equivalence class namely:
AttributeEquivalenceClass. In very simple words, a AttributeEquivalenceClass is
identified by an unique id and consists of a list of AttributeEquivalenceMembers.
Each AttributeEquivalenceMember is designed to identify attributes uniquely within the
whole query. The necessity of this arise since varno attributes are defined within
a single level of a query. Instead, here we want to identify each RTE_RELATION uniquely
and try to find equality among each RTE_RELATION's partition key.
Whenever we find an equality clause A = B, where both A and B originates from
relation attributes (i.e., not random expressions), we create an
AttributeEquivalenceClass to record this knowledge. If we later find another
equivalence B = C, we create another AttributeEquivalenceClass. Finally, we can
apply transitity rules and generate a new AttributeEquivalenceClass which includes
A, B and C.
Note that equality among the members are identified by the varattno and rteIdentity.
Each equality among RTE_RELATION is saved using an AttributeEquivalenceClass where
each member attribute is identified by a AttributeEquivalenceMember. In the final
step, we try generate a common attribute equivalence class that holds as much as
AttributeEquivalenceMembers whose attributes are a partition keys.
With this change, we start to error out if loaded citus binaries does not match
the available major version or installed citus extension version. In this case
we force user to restart the server or run ALTER EXTENSION depending on the
situation
Thought this looked slightly nicer than the default behavior.
Changed preventTransaction to concurrent to be clearer that this code
path presently affects CONCURRENTLY code only.
Coordinator code marks index as invalid as a base, set it as valid in a
transactional layer atop that base, then proceeds with worker commands.
If a worker command has problems, the rollback results in an index with
isvalid = false. If everything succeeds, the user sees a valid index.
Before this commit, in certain cases router planner allowed pushing
down JOINs that are not on the partition keys.
With @anarazel's suggestion, we change the logic to use uninstantiated
parameter. Previously, the planner was traversing on the restriction
information and once it finds the parameter, it was replacing it with
the shard range. With this commit, instead of traversing the restrict
infos, the planner explicitly checks for the equivalence of the relation
partition key with the uninstantiated parameter. If finds an equivalence,
it adds the restrictions. In this way, we have more control over the
queries that are pushed down.
Some tests relied on worker errors though local commands were invalid.
Fixed those by ensuring preconditions were met to have command work
correctly. Otherwise most test changes are related to slight changes
in local/remote error ordering.
When running under Enterprise, some of the GRANT commands and whatnot
are propagated. Guarding that section with a call to disable DDL prop.
fixes everything.
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.
Delete operation is blocked for any table distributed by hash using master_apply_delete_command. Suggested master_modify_multiple_shards command as a hint.
During later work the transaction debug output will change (as it will
in postgres 10), which makes it hard to see actual changes in the
INSERT ... SELECT ... test. Reduce to DEBUG2 after changing a debug
message to that log level.
This change ignores `citus.replication_model` setting and uses the
statement based replication in
- Tables distributed via the old `master_create_distributed_table` function
- Append and range partitioned tables, even if created via
`create_distributed_table` function
This seems like the easiest solution to #1191, without changing the existing
behavior and harming existing users with custom scripts.
This change also prevents RF>1 on streaming replicated tables on `master_create_worker_shards`
Prior to this change, `master_create_worker_shards` command was not checking
the replication model of the target table, thus allowing RF>1 with streaming
replicated tables. With this change, `master_create_worker_shards` errors
out on the case.
PostgreSQL 9.5.6 and 9.6.2 were released today and broke several tests
by adding TABLESPACE pg_default output to some DDL commands. Fixed all
occurrences.
cr: @anarazel
Add a call to RemoteTransactionBeginIfNecessary so that BEGIN is
actually sent to the remote connections. This means that ROLLBACK and
Ctrl-C are respected and don't leave the table in a partial state.
This change fixes the random failures on Travis, which is a bug introduced
with citus/#1124. Before this fix, travis was failing randomly on `check_multi_mx`
test schedule, specifically in the parallel group of `multi_mx_metadata`,
'multi_mx_modifications` and `multi_mx_modifying_xacts` tests. This change fixes this
by serializing these three test cases.
This change allows users to drop sequences on MX workers. Previously, Citus didn't allow dropping
sequences on MX workers because it could cause shards to be dropped if `DROP SEQUENCE ... CASCADE`
is used. We now allow that since allowing sequence creation but not dropping hurts user experience
and also may cause problems with custom Citus solutions.
- Break CheckShardPlacements into multiple functions (The most important
is MarkFailedShardPlacements), so that we can get rid of the global
CoordinatedTransactionUses2PC.
- Call MarkFailedShardPlacements in the router executor, so we mark
shards as invalid and stop using them while inside transaction blocks.
With this change DropShards function started to use new connection API. DropShards
function is used by DROP TABLE, master_drop_all_shards and master_apply_delete_command,
therefore all of these functions now support transactional operations. In DropShards
function, if we cannot reach a node, we mark shard state of related placements as
FILE_TO_DELETE and continue to drop remaining shards; however if any error occurs after
establishing the connection, we ROLLBACK whole operation.
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.
So far router planner had encapsulated different functionality in
MultiRouterPlanCreate. Modifications always go through router, selects
sometimes. Modifications always error out if the query is unsupported,
selects return NULL. Especially the error handling is a problem for
the upcoming extension of prepared statement support.
Split MultiRouterPlanCreate into CreateRouterPlan and
CreateModifyPlan, and change them to not throw errors.
Instead errors are now reported by setting the new
MultiPlan->plannigError.
Callers of router planner functionality now have to throw errors
themselves if desired, but also can skip doing so.
This is a pre-requisite for expanding prepared statement support.
While touching all those lines, improve a number of error messages by
getting them closer to the postgres error message guidelines.
This adds a replication_model GUC which is used as the replication
model for any new distributed table that is not a reference table.
With this change, tables with replication factor 1 are no longer
implicitly MX tables.
The GUC is similarly respected during empty shard creation for e.g.
existing append-partitioned tables. If the model is set to streaming
while replication factor is greater than one, table and shard creation
routines will error until this invalid combination is corrected.
Changing this parameter requires superuser permissions.
We changed error message which appears when user tries to execute outer join command and
that command requires repartitioning. Old error message mentioned about 1-to-1 shard
partitioning which may not be clear to user.
This enables proper transactional behaviour for copy and relaxes some
restrictions like combining COPY with single-row modifications. It
also provides the basis for relaxing restrictions further, and for
optionally allowing connection caching.
This change adds support for serial columns to be used with MX tables.
Prior to this change, sequences of serial columns were created in all
workers (for being able to create shards) but never used. With MX, we
need to set the sequences so that sequences in each worker create
unique values. This is done by setting the MINVALUE, MAXVALUE and
START values of the sequence.
This commit is intended to improve the error messages while planning
INSERT INTO .. SELECT queries. The main motivation for this change is
that we used to map multiple cases into a single message. With this change,
we added explicit error messages for many cases.
With this change, we start to delete placement of reference tables at given worker node
after master_remove_node UDF call. We remove placement metadata at master node but we do
not drop actual shard from the worker node. There are two reasons for that decision,
first, it is not critical to DROP the shards in the workers because Citus will ignore them
as long as node is removed from cluster and if we add that node back to cluster we will
DROP and recreate all reference tables. Second, if node is unreachable, it becomes
complicated to cover failure cases and have a transaction support.
Enables use views within distributed queries.
User can create and use a view on distributed tables/queries
as he/she would use with regular queries.
After this change router queries will have full support for views,
insert into select queries will support reading from views, not
writing into. Outer joins would have a limited support, and would
error out at certain cases such as when a view is in the inner side
of the outer join.
Although PostgreSQL supports writing into views under certain circumstances.
We disallowed that for distributed views.
In tests related to automatic reference table creation and deletion, there were some
tests whose output may change order thus creating inconsistent test results. With this
change we add ORDER BY clause to related tests to have consistent output.
CloseNodeConnections() is supposed to close connections to a given node.
However, before this commit it lacks to actually call PQFinish() on the
connections. Using CloseConnection() handles closing and all other necessary
actions.
With this change we start to error out on router planner queries where a common table
expression with data-modifying statement is present. We already do not support if
there is a data-modifying statement using result of the CTE, now we also error out
if CTE itself is data-modifying statement.
Remove the router specific transaction and shard management, and
replace it with the new placement connection API. This mostly leaves
behaviour alone, except that it is now, inside a transaction, legal to
select from a shard to which no pre-existing connection exists.
To simplify code the code handling task executions for select and
modify has been split into two - the previous coding was starting to
get confusing due to the amount of only conditionally applicable code.
Modification connections & transactions are now always established in
parallel, not just for reference tables.
With this change, we start to replicate all reference tables to the new node when new node
is added to the cluster with master_add_node command. We also update replication factor
of reference table's colocation group.
Since we will now replicate reference tables each time we add node, we need to ensure
that test space is clean in terms of reference tables before any add node operation.
For this purpose we had to change order of multi_drop_extension test which caused
change of some of the colocation ids.
With this change we introduce new UDF, upgrade_to_reference_table, which can be used to
upgrade existing broadcast tables reference tables. For upgrading, we require that given
table contains only one shard.
Renamed FindShardIntervalIndex() to ShardIndex() and added binary search
capability. It used to assume that hash partition tables are always
uniformly distributed which is not true if upcoming tenant isolation
feature is applied. This commit also reduces code duplication.
Router planner already handles cases when all shards
are pruned out. This is about missing test cases. Notice that
"column is null" and "column = null" have different shard
pruning behavior.
We have one replication of reference table for each node. Therefore all problems with
replication factor > 1 also applies to reference table. As a solution we will not allow
foreign keys on reference tables. It is not possible to define foreign key from, to or
between reference tables.
Previously, we errored out if non-user tries to SELECT query for some metadata tables. It
seems that we already GRANT SELECT access to some metadata tables but not others. With
this change, we GRANT SELECT access to all existing Citus metadata tables.
* Add get_distribution_value_shardid UDF
With this UDF users can now map given distribution value to shard id. We mostly hide
shardids from users to prevent unnecessary complexity but some power users might need
to know about which entry/value is stored in which shard for maintanence purposes.
Signature of this UDF is as follows;
bigint get_distribution_value_shardid(table_name regclass, distribution_value anyelement)
Onder Kalaci
mehmet furkan şahin
Dimitri Fontaine
Marco Slot
velioglu
Hadi Moshayedi
Brian Cloutier
Murat Tuncer
Brian Cloutier
Burak Yucesoy
Murat Tuncer
Metin Doslu
Markus Sintonen
Brian Cloutier
Marco Slot
Hadi Moshayedi
Jason Petersen
Eren Başak
Andres Freund
Jason Petersen
jmunsch
Burak Velioglu
Burak Velioglu