When executing queries with citus.task_executor = 'real-time', query
execution could, so far, spend a significant amount of time
sleeping. That's because we were
a) sleeping after several phases of query execution, even if we're not
waiting for network IO
b) sleeping for a fixed amount of time when waiting for network IO;
often a lot longer than actually required.
Just reducing the amount of time slept isn't a real solution, because
that just increases CPU usage.
Instead have the real-time executor's ManageTaskExecution return whether
a task is currently being processed, waiting for reads or writes, or
failed. When all tasks are waiting for IO use poll() to wait for IO
readyness.
That requires to slightly redefine how connection timeouts are handled:
before we counted the number of times ManageTaskExecution() was called,
and compared that with the timeout divided by the task check
interval. That, if processing of tasks took a while, could significantly
increase the time till a timeout occurred. Because it was based on the
ManageTaskExecution() being called on a constant interval, this approach
isn't feasible anymore. Instead measure the actual time since
connection establishment was started. That could in theory, if task
processing takes a very long time, lead to few passes over
PQconnectPoll().
The problem of sleeping too much also exists for the 'task-tracker'
executor, but is generally less problematic there, as processing the
individual tasks usually will take longer. That said, for e.g. the
regression tests it'd be helpful to use a similar approach.
Single table repartition subqueries now support count(distinct column)
and count(distinct (case when ...)) expressions. Repartition query
extracts column used in aggregate expression and adds them to target
list and group by list, master query stays the same (count (distinct ...))
but attribute numbers inside the aggregate expression is modified to
reflect changes in repartition query.
This commit adds a fast shard pruning path for INSERTs on
hash-partitioned tables. The rationale behind this change is
that if there exists a sorted shard interval array, a single
index lookup on the array allows us to find the corresponding
shard interval. As mentioned above, we need a sorted
(wrt shardminvalue) shard interval array. Thus, this commit
updates shardIntervalArray to sortedShardIntervalArray in the
metadata cache. Then uses the low-level API that is defined in
multi_copy to handle the fast shard pruning.
The performance impact of this change is more apparent as more
shards exist for a distributed table. Previous implementation
was relying on linear search through the shard intervals. However,
this commit relies on constant lookup time on shard interval
array. Thus, the shard pruning becomes less dependent on the
shard count.
Andres Freund
Murat Tuncer
Onder Kalaci
Jason Petersen