We should not recursively plan an already routable plannable query. An
example of this is (SELECT * FROM local JOIN (SELECT * FROM dist) d1
USING(a));
So we let the recursive planner do all of its work and at the end we
convert the final query to to handle unsupported joins. While doing each
conversion, we check if it is router plannable, if so we stop.
Only consider range table entries that are in jointree
If a range table is not in jointree then there is no point in
considering that because we are trying to convert range table entries to
subqueries for join use case.
Check equality in quals
We want to recursively plan distributed tables only if they have an
equality filter on a unique column. So '>' and '<' operators will not
trigger recursive planning of distributed tables in local-distributed
table joins.
Recursively plan distributed table only if the filter is constant
If the filter is not a constant then the join might return multiple rows
and there is a chance that the distributed table will return huge data.
Hence if the filter is not constant we choose to recursively plan the
local table.
When doing local-distributed table joins we convert one of them to
subquery. The current policy is that we convert distributed tables to
subquery if it has a unique index on a column that has unique
index(primary key also has a unique index).
UPDATEs on partitioned tables that affect only row partitions should
succeed, the rest should fail.
Also rename CStoreScan to ColumnarScan to make the error message more
relevant.
When Citus needs to parallelize queries on the local node (e.g., the node
executing the distributed query and the shards are the same), we need to
be mindful about the connection management. The reason is that the client
backends that are running distributed queries are competing with the client
backends that Citus initiates to parallelize the queries in order to get
a slot on the max_connections.
In that regard, we implemented a "failover" mechanism where if the distributed
queries cannot get a connection, the execution failovers the tasks to the local
execution.
The failover logic is follows:
- As the connection manager if it is OK to get a connection
- If yes, we are good.
- If no, we fail the workerPool and the failure triggers
the failover of the tasks to local execution queue
The decision of getting a connection is follows:
/*
* For local nodes, solely relying on citus.max_shared_pool_size or
* max_connections might not be sufficient. The former gives us
* a preview of the future (e.g., we let the new connections to establish,
* but they are not established yet). The latter gives us the close to
* precise view of the past (e.g., the active number of client backends).
*
* Overall, we want to limit both of the metrics. The former limit typically
* kics in under regular loads, where the load of the database increases in
* a reasonable pace. The latter limit typically kicks in when the database
* is issued lots of concurrent sessions at the same time, such as benchmarks.
*/
When distributing a columnar table, as well as changing options on a distributed columnar table, this patch will forward the settings from the coordinator to the workers.
For propagating options changes on an already distributed table this change is pretty straight forward. Before applying the change in options locally we will create a `DDLJob` that contains a call to `alter_columnar_table_set(...)` for every shard placement with all settings of the current table. This goes both for setting an option as well as resetting. This will reset the values to the defaults configured on the coordinator. Having the effect that the coordinator is authoritative on the settings and makes sure the shards have the same settings set as the table on the coordinator.
When a columnar table is distributed it is using the `TableDDLCommand` infra structure to create a new kind of `TableDDLCommand`. This new type, called a `TableDDLCommandFunction` contains a context and 2 function pointers to execute. One function returns the command as applied on the table, the second function will return the sql command to apply to a shard with a given shard id. The schema name is ignored as it will use the fully qualified name of the shard in the same schema as the base table.
Multi-row execution already uses sequential execution. When shards
are local, using local execution is profitable as it avoids
an extra connection establishment to the local node.
Join test gets too many clients error too frequently hence we should
not run anything concurrently with that. Hopefully this will fix the
flakiness of test.
This is to avoid flaky changes like the following in test outputs:
-CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.00 s.
+CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.02 s.
Columnar options were by accident linked to the relfilenode instead of the regclass/relation oid. This PR moves everything related to columnar options to their own catalog table.
Considering the adaptive connection management
improvements that we plan to roll soon, it makes it
very helpful to know the number of active client
backends.
We are doing this addition to simplify yhe adaptive connection
management for single node Citus. In single node Citus, both the
client backends and Citus parallel queries would compete to get
slots on Postgres' `max_connections` on the same Citus database.
With adaptive connection management, we have the counters for
Citus parallel queries. That helps us to adaptively decide
on the remote executions pool size (e.g., throttle connections
if necessary).
However, we do not have any counters for the total number of
client backends on the database. For single node Citus, we
should consider all the client backends, not only the remote
connections that Citus does.
Of course Postgres internally knows how many client
backends are active. However, to get that number Postgres
iterates over all the backends. For examaple, see [pg_stat_get_db_numbackends](8e90ec5580/src/backend/utils/adt/pgstatfuncs.c (L1240))
where Postgres iterates over all the backends.
For our purpuses, we need this information on every connection
establishment. That's why we cannot affort to do this kind of
iterattion.
CitusTableTypeIdList() function iterates on all the entries of pg_dist_partition
and loads all the metadata in to the cache. This can be quite memory intensive
especially when there are lots of distributed tables.
When partitioned tables are used, it is common to have many distributed tables
given that each partition also becomes a distributed table.
CitusTableTypeIdList() is used on every CREATE TABLE .. PARTITION OF.. command
as well. It means that, anytime a partition is created, Citus loads all the
metadata to the cache. Note that Citus typically only loads the accessed table's
metadata to the cache.
* Move local execution after the remote execution
Before this commit, when both local and remote tasks
exist, the executor was starting the execution with
local execution. There is no strict requirements on
this.
Especially considering the adaptive connection management
improvements that we plan to roll soon, moving the local
execution after to the remote execution makes more sense.
The adaptive connection management for single node Citus
would look roughly as follows:
- Try to connect back to the coordinator for running
parallel queries.
- If succeeds, go on and execute tasks in parallel
- If fails, fallback to the local execution
So, we'll use local execution as a fallback mechanism. And,
moving it after to the remote execution allows us to implement
such further scenarios.
Before this commit, we let AdaptiveExecutorPreExecutorRun()
to be effective multiple times on every FETCH on cursors.
That does not affect the correctness of the query results,
but adds significant overhead.
TableAM API doesn't allow us to pass around a state variable along all of the tuple inserts belonging to the same command. We require this in columnar store, since we batch them, and when we have enough rows we flush them as stripes.
To do that, we keep a (relfilenode) -> stack of (subxact id, TableWriteState) global mapping.
**Inserts**
Whenever we want to insert a tuple, we look up for the relation's relfilenode in this mapping. If top of the stack matches current subtransaction, we us the existing TableWriteState. Otherwise, we allocate a new TableWriteState and push it on top of stack.
**(Sub)Transaction Commit/Aborts**
When the subtransaction or transaction is committed, we flush and pop all entries matching current SubTransactionId.
When the subtransaction or transaction is committed, we pop all entries matching current SubTransactionId and discard them without flushing.
**Reads**
Since we might have unwritten rows which needs to be read by a table scan, we flush write states on SELECTs. Since flushing the write state of upper transactions in a subtransaction will cause metadata being written in wrong subtransaction, we ERROR out if any of the upper subtransactions have unflushed rows.
**Table Drops**
We record in which subtransaction the table was dropped. When committing a subtransaction in which table was dropped, we propagate the drop to upper transaction. When aborting a subtransaction in which table was dropped, we mark table as not deleted.
* Update failure test dependencies
There was a security alert for cryptography. The vulnerability was fixed
in 3.2.0. The vulnebarility:
"RSA decryption was vulnerable to Bleichenbacher timing vulnerabilities,
which would impact people using RSA decryption in online scenarios."
The fix:
58494b41d6
It wasn't enough to only update crpytography because mitm was
incompatible with the new version, so mitm is also upgraded.
The steps to do in local:
python -m pip install -U cryptography
python -m pip install -U mitmproxy
When a relation is used on an OUTER JOIN with FALSE filters,
set_rel_pathlist_hook may not be called for the table.
There might be other cases as well, so do not rely on the hook
for classification of the tables.
Aliases that postgres choose for partitioned tables in explain output
might change in different pg versions, so normalize them and remove
the alternative test output
* Fix incorrect join related fields
Ruleutils expect to give the original index of join columns hence we
should consider the dropped columns while setting the fields in
SetJoinRelatedFieldsCompat.
* add some more tests for joins
* Move tests to join.sql and create a utility function
Disallow `ON TRUE` outer joins with reference & distributed tables
when reference table is outer relation by fixing the logic bug made
when calling `LeftListIsSubset` function.
Also, be more defensive when removing duplicate join restrictions
when join clause is empty for non-inner joins as they might still
contain useful information for non-inner joins.
It seems like Postgres could call set_rel_pathlist() for
the same relation multiple times. This breaks the logic
where we assume relationCount eqauls to the number of
entries in relationRestrictionList.
In summary, relationRestrictionList may contain duplicate
entries.
With this commit, we make sure that local execution adds the
intermediate result size as the distributed execution adds. Plus,
it enforces the citus.max_intermediate_result_size value.
Before this commit, the logic was:
- As long as the outer side of the JOIN is not a JOIN (e.g., relation
or subquery etc.), we check for the existence of any recurring
tuples. There were two implications of this decision.
First, even if a subquery which is on the outer side contains
distributed table JOIN reference table, Citus would unnecessarily throw
an error. Note that, the JOIN inside the subquery would already
be going to be tested recursively. But, as long as that check
passes, there is no reason for the upper JOIN to fail. An example, which
used to fail and now works:
SELECT * FROM (SELECT * FROM dist JOIN ref) as foo LEFT JOIN dist;
Second, certain JOINs, especially with ON (true) conditions were not
represented as Citus expects the JOINs to be in the format
DeferredErrorIfUnsupportedRecurringTuplesJoin().
Use short lived per-tuple context in citus_evaluate_expr like
(pg) evaluate_expr does.
We should not use planState->ExprContext when evaluating expressions
as it might lead to freeing the same executor twice (first one happens
in citus_evaluate_expr itself and the other one happens when postgres
doing clean-up for the top level executor state), which in turn might
cause seg.faults.
However, now as we don't have necessary planState info to evaluate
prepared statements, we also add planState->es_param_list_info to
per-tuple ExprContext.
Sait Talha Nisanci
Onder Kalaci
Marco Slot
Hadi Moshayedi
jeff-davis
Jeff Davis
Ahmet Gedemenli
Hadi Moshayedi
Nils Dijk
Halil Ozan Akgül
Önder Kalacı
Onur Tirtir
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