Previously a limitation in the shard pruning logic caused multi distribution value queries to always go into all the shards/workers whenever query also used OR conditions in WHERE clause.
Related to https://github.com/citusdata/citus/issues/2593 and https://github.com/citusdata/citus/issues/1537
There was no good workaround for this limitation. The limitation caused quite a bit of overhead with simple queries being sent to all workers/shards (especially with setups having lot of workers/shards).
An example of a previous plan which was inadequately pruned:
```
EXPLAIN SELECT count(*) FROM orders_hash_partitioned
WHERE (o_orderkey IN (1,2)) AND (o_custkey = 11 OR o_custkey = 22);
QUERY PLAN
---------------------------------------------------------------------
Aggregate (cost=0.00..0.00 rows=0 width=0)
-> Custom Scan (Citus Adaptive) (cost=0.00..0.00 rows=0 width=0)
Task Count: 4
Tasks Shown: One of 4
-> Task
Node: host=localhost port=xxxxx dbname=regression
-> Aggregate (cost=13.68..13.69 rows=1 width=8)
-> Seq Scan on orders_hash_partitioned_630000 orders_hash_partitioned (cost=0.00..13.68 rows=1 width=0)
Filter: ((o_orderkey = ANY ('{1,2}'::integer[])) AND ((o_custkey = 11) OR (o_custkey = 22)))
(9 rows)
```
After this commit the task count is what one would expect from the query defining multiple distinct values for the distribution column:
```
EXPLAIN SELECT count(*) FROM orders_hash_partitioned
WHERE (o_orderkey IN (1,2)) AND (o_custkey = 11 OR o_custkey = 22);
QUERY PLAN
---------------------------------------------------------------------
Aggregate (cost=0.00..0.00 rows=0 width=0)
-> Custom Scan (Citus Adaptive) (cost=0.00..0.00 rows=0 width=0)
Task Count: 2
Tasks Shown: One of 2
-> Task
Node: host=localhost port=xxxxx dbname=regression
-> Aggregate (cost=13.68..13.69 rows=1 width=8)
-> Seq Scan on orders_hash_partitioned_630000 orders_hash_partitioned (cost=0.00..13.68 rows=1 width=0)
Filter: ((o_orderkey = ANY ('{1,2}'::integer[])) AND ((o_custkey = 11) OR (o_custkey = 22)))
(9 rows)
```
"Core" of the pruning logic works as previously where it uses `PrunableInstances` to queue ORable valid constraints for shard pruning.
The difference is that now we build a compact internal representation of the query expression tree with PruningTreeNodes before actual shard pruning is run.
Pruning tree nodes represent boolean operators and the associated constraints of it. This internal format allows us to have compact representation of the query WHERE clauses which allows "core" pruning logic to work with OR-clauses correctly.
For example query having
`WHERE (o_orderkey IN (1,2)) AND (o_custkey=11 OR (o_shippriority > 1 AND o_shippriority < 10))`
gets transformed into:
1. AND(o_orderkey IN (1,2), OR(X, AND(X, X)))
2. AND(o_orderkey IN (1,2), OR(X, X))
3. AND(o_orderkey IN (1,2), X)
Here X is any set of unknown condition(s) for shard pruning.
This allow the final shard pruning to correctly recognize that shard pruning is done with the valid condition of `o_orderkey IN (1,2)`.
Another example with unprunable condition in query
`WHERE (o_orderkey IN (1,2)) OR (o_custkey=11 AND o_custkey=22)`
gets transformed into:
1. OR(o_orderkey IN (1,2), AND(X, X))
2. OR(o_orderkey IN (1,2), X)
Which is recognized as unprunable due to the OR condition between distribution column and unknown constraint -> goes to all shards.
Issue https://github.com/citusdata/citus/issues/1537 originally suggested transforming the query conditions into a full disjunctive normal form (DNF),
but this process of transforming into DNF is quite a heavy operation. It may "blow up" into a really large DNF form with complex queries having non trivial `WHERE` clauses.
I think the logic for shard pruning could be simplified further but I decided to leave the "core" of the shard pruning untouched.
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.
We used to disable router planner and executor
when task executor is set to task-tracker.
This change enables router planning and execution
at all times regardless of task execution mode.
We are introducing a hidden flag enable_router_execution
to enable/disable router execution. Its default value is
true. User may disable router planning by setting it to false.
Two sets of tests are fixed by this change:
* multi_agg_approximate_distinct
* those in multi_task_tracker_extra_schedule
The first broke when we renamed stage to load in many files and was
never being run because the HyperLogLog extension wasn't easily
available in Debian. Now it's in our repo, so we install it and run
the test. I removed the distinct HLL target in favor of just always
running it and providing an output variant to handle when the extension
is absent. Basically, if PostgreSQL thinks HLL is available, the test
installs it and runs normally, otherwise the absent variant is used.
The second broke when I removed a test variant, erroneously believing
it to be related to an older Citus version. I've added a line in that
test to clarify why the variant is necessary (a practice we should
widely adopt).
"Staging table" will be the only valid use of 'stage' from now on, we
will now say "load" when talking about data ingestion. If creation of
shards is its own step, we'll just say "shard creation".
Fixes#271
This change sets ShardIds and JobIds for each test case. Before this change,
when a new test that somehow increments Job or Shard IDs is added, then
the tests after the new test should be updated.
ShardID and JobID sequences are set at the beginning of each file with the
following commands:
```
ALTER SEQUENCE pg_catalog.pg_dist_shardid_seq RESTART 290000;
ALTER SEQUENCE pg_catalog.pg_dist_jobid_seq RESTART 290000;
```
ShardIds and JobIds are multiples of 10000. Exceptions are:
- multi_large_shardid: shardid and jobid sequences are set to much larger values
- multi_fdw_large_shardid: same as above
- multi_join_pruning: Causes a race condition with multi_hash_pruning since
they are run in parallel.
This commit fixes failures happen during check-full. The change does make
clean seperation of executor types in certain places to keep the outputs
stable.
Fixes issue #258
Prior to this change, Citus gives a deceptive NOTICE message when a query
including ANY or ALL on a non-partition column is issued on a hash
partitioned table.
Let the github_events table be hash-distributed on repo_id column. Then,
issuing this query:
SELECT count(*) FROM github_events WHERE event_id = ANY ('{1,2,3}')
Gives this message:
NOTICE: cannot use shard pruning with ANY (array expression)
HINT: Consider rewriting the expression with OR clauses.
Note that since event_id is not the partition column, shard pruning would
not be applied in any case. However, the NOTICE message would be valid
and be given if the ANY clause would have been applied on repo_id column.
Reviewer: Murat Tuncer
Markus Sintonen
Philip Dubé
Onder Kalaci
velioglu
Marco Slot
Andres Freund
Murat Tuncer
Jason Petersen
Eren