fix(coord): Fix query planning to properly support long range subqueries #1976
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vishramachandran
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alextheimer,
amolnayak311,
kvpetrov,
sandeep6189,
sherali42 and
yu-shipit
old plan ``` T~PeriodicSamplesMapper(start=1633913330000, step=300000, end=1634755730000, window=Some(900000), functionId=Some(LastOverTimeIsMadOutlier), rawSource=false, offsetMs=None) -FA1~StaticFuncArgs(3.0,RangeParams(1633913330,300,1634777330)) -FA2~StaticFuncArgs(1.0,RangeParams(1633913330,300,1634777330)) -T~AggregatePresenter(aggrOp=Sum, aggrParams=List(), rangeParams=RangeParams(1633912500,300,1634755500)) --E~LocalPartitionReduceAggregateExec(aggrOp=Sum, aggrParams=List()) on ActorPlanDispatcher(Actor[akka://default/system/testProbe-1#5785863],downsample) ---T~AggregateMapReduce(aggrOp=Sum, aggrParams=List(), without=List(), by=List()) ----T~PeriodicSamplesMapper(start=1633912500000, step=300000, end=1634755500000, window=Some(300000), functionId=Some(Rate), rawSource=true, offsetMs=None) -----E~MultiSchemaPartitionsExec(dataset=timeseries, shard=0, chunkMethod=TimeRangeChunkScan(1633912200000,1634755500000), filters=List(ColumnFilter(_ws_,Equals(demo)), ColumnFilter(_ns_,Equals(localNs)), ColumnFilter(instance,Equals(Inst-1)), ColumnFilter(_metric_,Equals(foo))), colName=None, schema=None) on ActorPlanDispatcher(Actor[akka://default/system/testProbe-1#5785863],downsample) ---T~AggregateMapReduce(aggrOp=Sum, aggrParams=List(), without=List(), by=List()) ----T~PeriodicSamplesMapper(start=1633912500000, step=300000, end=1634755500000, window=Some(300000), functionId=Some(Rate), rawSource=true, offsetMs=None) -----E~MultiSchemaPartitionsExec(dataset=timeseries, shard=1, chunkMethod=TimeRangeChunkScan(1633912200000,1634755500000), filters=List(ColumnFilter(_ws_,Equals(demo)), ColumnFilter(_ns_,Equals(localNs)), ColumnFilter(instance,Equals(Inst-1)), ColumnFilter(_metric_,Equals(foo))), colName=None, schema=None) on ActorPlanDispatcher(Actor[akka://default/system/testProbe-1#5785863],downsample) ```
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Pull Request checklist
Current behavior :
PromQL with subquery that span both raw and downsample clusters, is entirely sent to the downsample cluster.
New behavior :
We delegate the planning of subquery back to the root planner which enables us to properly plan the subquery over long range, and then we apply the windowing function over the results.