Filed https://issues.apache.org/jira/browse/DRILL-7277
On Fri, May 24, 2019 at 11:56 PM Ted Dunning <[email protected]> wrote: > > Good eye to spot the issue with redundant ordering. No, I don't need both. > The reason I wound up with two is due to my building and rebuilding the > query many times. Essentially, a cut and paste error led to that. > > This is good news is it is much less likely that most (sensible) users run > into this. > > > > On Fri, May 24, 2019 at 5:26 PM Aman Sinha <[email protected]> wrote: > >> The changing of the column order in the SELECT should not materially >> affect >> the planning, so this does look like a bug. I was able to repro it with >> a >> simpler example (without the group-by): >> select row_number() over (order by department_id desc) r, >> department_id >> from (select department_id >> from cp.`employee.json` >> order by department_id desc) ; >> >> Error: SYSTEM ERROR: CannotPlanException: Node >> [rel#3170:Subset#4.LOGICAL.ANY([]).[1 DESC]] could not be implemented; >> planner state: >> >> I believe the 2 ORDER BYs are causing an issue with the plan generation >> but >> more analysis is needed by looking at the Calcite trace. However, that >> brings up the question of why you need 2 ORDER BYs ? If the ROW_NUMBER() >> is already doing an ORDER BY DESC on the exact same column, *the subquery >> ORDER BY is redundant.* If you remove it, the query runs OK... >> >> apache drill> *select* row_number() *over* (*order* *by* cnt *desc*) r, >> cnt >> >> . .semicolon> *from* (*select* *count*(1) *as* cnt >> >> . . . . . .)> *from* cp.`employee.json` >> >> . . . . . .)> *group* *by* department_id); >> >> *+----+-----+* >> >> *| **r ** | **cnt** |* >> >> *+----+-----+* >> >> *| *1 * | *268* |* >> >> *| *2 * | *264* |* >> >> *| *3 * | *226* |* >> >> *| *4 * | *222* |* >> >> *| *5 * | *100* |* >> >> *| *6 * | *32 * |* >> >> *| *7 * | *16 * |* >> >> *| *8 * | *9 * |* >> >> *| *9 * | *7 * |* >> >> *| *10* | *5 * |* >> >> *| *11* | *4 * |* >> >> *| *12* | *2 * |* >> >> *+----+-----+* >> >> 12 rows selected >> >> In any case if you can file a JIRA for the original problem that would be >> good. >> >> Thanks. >> >> On Fri, May 24, 2019 at 2:57 PM Ted Dunning <[email protected]> >> wrote: >> >> > I have a bunch of data that I want to group up and look at the counts. >> In >> > order to get a row number for plotting, I tried a window function. >> > >> > The data consists of about 7,2 million rows accessed via a view[1]. The >> > columns are pretty much all untyped[2]. >> > >> > This query works great: >> > >> > >> > *with * >> > *t0 as (* >> > * select count(1) cnt* >> > * from dfs.flt.`flights-2018-01.csv` * >> > * group by columns[5]* >> > * order by cnt desc)* >> > *select cnt, row_number() over (order by cnt desc) r* >> > *from t0* >> > >> > >> > But if I change the order of the columns like this: >> > >> > *with * >> > *t0 as (* >> > * select count(1) cnt* >> > * from dfs.flt.`flights-2018-01.csv` * >> > * group by columns[5]* >> > * order by cnt desc)* >> > *select row_number() over (order by cnt desc) r, cnt* >> > *from t0* >> > >> > >> > I get the error below in query planning. That seems so very wrong. >> > >> > Any ideas? I know I can just avoid the issue, but I was hoping for some >> > insight. >> > >> > For extra oddness points, if I use a common table expression and invert >> the >> > field order, I get what I want with no error: >> > >> > >> > *with * >> > *t0 as (* >> > * select count(1) cnt* >> > * from dfs.flt.`flights-2018-01.csv` * >> > * group by columns[5]* >> > * order by cnt desc),* >> > *t1 as (* >> > * select cnt, row_number() over (order by cnt desc) r * >> > * from t0)* >> > *select r, cnt* >> > *from t1* >> > >> > >> > This means that the planner is not rewriting this to eliminate the >> common >> > table before planning. >> > >> > >> > java.sql.SQLException: [MapR][DrillJDBCDriver](500165) Query execution >> > error. Details: SYSTEM ERROR: CannotPlanException: Node >> > [rel#18615:Subset#8.LOGICAL.ANY([]).[1 DESC]] could not be implemented; >> > planner state: Root: rel#18615:Subset#8.LOGICAL.ANY([]).[1 DESC] >> Original >> > rel: LogicalProject(subset=[rel#18615:Subset#8.LOGICAL.ANY([]).[1 >> DESC]], >> > r=[$1], cnt=[$0]): rowcount = 10.0, cumulative cost = {10.0 rows, 20.0 >> cpu, >> > 0.0 io, 0.0 network, 0.0 memory}, id = 18613 >> > LogicalWindow(subset=[rel#18612:Subset#7.NONE.ANY([]).[1 DESC]], >> > window#0=[window(partition {} order by [0 DESC] rows between UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])]): rowcount = 10.0, >> > cumulative cost = {10.0 rows, 20.0 cpu, 0.0 io, 0.0 network, 0.0 >> memory}, >> > id = 18611 LogicalSort(subset=[rel#18610:Subset#6.NONE.ANY([]).[0 >> DESC]], >> > sort0=[$0], dir0=[DESC]): rowcount = 10.0, cumulative cost = {10.0 rows, >> > 92.10340371976184 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18609 >> > LogicalProject(subset=[rel#18608:Subset#5.NONE.ANY([]).[]], cnt=[$1]): >> > rowcount = 10.0, cumulative cost = {10.0 rows, 10.0 cpu, 0.0 io, 0.0 >> > network, 0.0 memory}, id = 18607 >> > LogicalAggregate(subset=[rel#18606:Subset#4.NONE.ANY([]).[]], >> group=[{0}], >> > cnt=[COUNT($1)]): rowcount = 10.0, cumulative cost = {11.25 rows, 0.0 >> cpu, >> > 0.0 io, 0.0 network, 0.0 memory}, id = 18605 >> > LogicalProject(subset=[rel#18604:Subset#3.NONE.ANY([]).[]], >> > dest_airport_id=[$5], $f1=[1]): rowcount = 100.0, cumulative cost = >> {100.0 >> > rows, 200.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18603 >> > LogicalProject(subset=[rel#18602:Subset#2.NONE.ANY([]).[]], >> fl_date=[$0], >> > op_unique_carrier=[$1], tail_num=[$2], op_carrier_fl_num=[$3], >> > origin_airport_id=[$4], dest_airport_id=[$5], crs_dep_time=[$6], >> > dep_time=[$7], dep_delay=[$8], taxi_out=[$9], wheels_off=[$10], >> > wheels_on=[$11], taxi_in=[$12], arr_time=[$13], arr_delay=[$14], >> > air_time=[$15], distance=[$16]): rowcount = 100.0, cumulative cost = >> {100.0 >> > rows, 1700.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18601 >> > LogicalProject(subset=[rel#18600:Subset#1.NONE.ANY([]).[]], >> > EXPR$0=[ITEM($1, 0)], EXPR$1=[ITEM($1, 1)], EXPR$2=[ITEM($1, 2)], >> > EXPR$3=[ITEM($1, 3)], EXPR$4=[ITEM($1, 4)], EXPR$5=[ITEM($1, 5)], >> > EXPR$6=[ITEM($1, 6)], EXPR$7=[ITEM($1, 7)], EXPR$8=[ITEM($1, 8)], >> > EXPR$9=[ITEM($1, 9)], EXPR$10=[ITEM($1, 10)], EXPR$11=[ITEM($1, 11)], >> > EXPR$12=[ITEM($1, 12)], EXPR$13=[ITEM($1, 13)], EXPR$14=[ITEM($1, 14)], >> > EXPR$15=[ITEM($1, 15)], EXPR$16=[ITEM($1, 16)]): rowcount = 100.0, >> > cumulative cost = {100.0 rows, 1700.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory}, id = 18599 >> > EnumerableTableScan(subset=[rel#18598:Subset#0.ENUMERABLE.ANY([]).[]], >> > table=[[dfs, flt, flights-2018-*.csv]]): rowcount = 100.0, cumulative >> cost >> > = {100.0 rows, 101.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18507 >> > Sets: Set#0, type: RecordType(DYNAMIC_STAR **, ANY columns) >> > rel#18598:Subset#0.ENUMERABLE.ANY([]).[], best=rel#18507, >> > importance=0.4304672100000001 >> > rel#18507:EnumerableTableScan.ENUMERABLE.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv]), rowcount=100.0, cumulative cost={100.0 rows, 101.0 >> > cpu, 0.0 io, 0.0 network, 0.0 memory} >> > rel#18644:Subset#0.LOGICAL.ANY([]).[], best=rel#18653, >> > importance=0.28936962450000003 >> > rel#18653:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, columns=[`**`, >> > `columns`], files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8.370632E10 cpu, 0.0 io, 0.0 network, >> 0.0 >> > memory} Set#1, type: RecordType(ANY EXPR$0, ANY EXPR$1, ANY EXPR$2, ANY >> > EXPR$3, ANY EXPR$4, ANY EXPR$5, ANY EXPR$6, ANY EXPR$7, ANY EXPR$8, ANY >> > EXPR$9, ANY EXPR$10, ANY EXPR$11, ANY EXPR$12, ANY EXPR$13, ANY EXPR$14, >> > ANY EXPR$15, ANY EXPR$16) rel#18600:Subset#1.NONE.ANY([]).[], best=null, >> > importance=0.4782969000000001 >> > >> > >> rel#18599:LogicalProject.NONE.ANY([]).[](input=rel#18598:Subset#0.ENUMERABLE.ANY([]).[],EXPR$0=ITEM($1, >> > 0),EXPR$1=ITEM($1, 1),EXPR$2=ITEM($1, 2),EXPR$3=ITEM($1, >> 3),EXPR$4=ITEM($1, >> > 4),EXPR$5=ITEM($1, 5),EXPR$6=ITEM($1, 6),EXPR$7=ITEM($1, >> 7),EXPR$8=ITEM($1, >> > 8),EXPR$9=ITEM($1, 9),EXPR$10=ITEM($1, 10),EXPR$11=ITEM($1, >> > 11),EXPR$12=ITEM($1, 12),EXPR$13=ITEM($1, 13),EXPR$14=ITEM($1, >> > 14),EXPR$15=ITEM($1, 15),EXPR$16=ITEM($1, 16)), rowcount=100.0, >> cumulative >> > cost={inf} >> > >> > >> rel#18601:LogicalProject.NONE.ANY([]).[](input=rel#18600:Subset#1.NONE.ANY([]).[],fl_date=$0,op_unique_carrier=$1,tail_num=$2,op_carrier_fl_num=$3,origin_airport_id=$4,dest_airport_id=$5,crs_dep_time=$6,dep_time=$7,dep_delay=$8,taxi_out=$9,wheels_off=$10,wheels_on=$11,taxi_in=$12,arr_time=$13,arr_delay=$14,air_time=$15,distance=$16), >> > rowcount=100.0, cumulative cost={inf} >> > rel#18636:Subset#1.LOGICAL.ANY([]).[], best=rel#18649, >> > importance=0.28936962450000003 >> > >> > >> rel#18649:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18648:Subset#10.LOGICAL.ANY([]).[],EXPR$0=ITEM($0, >> > 0),EXPR$1=ITEM($0, 1),EXPR$2=ITEM($0, 2),EXPR$3=ITEM($0, >> 3),EXPR$4=ITEM($0, >> > 4),EXPR$5=ITEM($0, 5),EXPR$6=ITEM($0, 6),EXPR$7=ITEM($0, >> 7),EXPR$8=ITEM($0, >> > 8),EXPR$9=ITEM($0, 9),EXPR$10=ITEM($0, 10),EXPR$11=ITEM($0, >> > 11),EXPR$12=ITEM($0, 12),EXPR$13=ITEM($0, 13),EXPR$14=ITEM($0, >> > 14),EXPR$15=ITEM($0, 15),EXPR$16=ITEM($0, 16)), rowcount=8370632.0, >> > cumulative cost={1.6741264E7 rows, 5.77573608E8 cpu, 0.0 io, 0.0 >> network, >> > 0.0 memory} >> > >> > >> rel#18650:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18644:Subset#0.LOGICAL.ANY([]).[],EXPR$0=ITEM($1, >> > 0),EXPR$1=ITEM($1, 1),EXPR$2=ITEM($1, 2),EXPR$3=ITEM($1, >> 3),EXPR$4=ITEM($1, >> > 4),EXPR$5=ITEM($1, 5),EXPR$6=ITEM($1, 6),EXPR$7=ITEM($1, >> 7),EXPR$8=ITEM($1, >> > 8),EXPR$9=ITEM($1, 9),EXPR$10=ITEM($1, 10),EXPR$11=ITEM($1, >> > 11),EXPR$12=ITEM($1, 12),EXPR$13=ITEM($1, 13),EXPR$14=ITEM($1, >> > 14),EXPR$15=ITEM($1, 15),EXPR$16=ITEM($1, 16)), rowcount=8370632.0, >> > cumulative cost={1.6741264E7 rows, 8.4275522976E10 cpu, 0.0 io, 0.0 >> > network, 0.0 memory} >> > >> > >> rel#18651:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18636:Subset#1.LOGICAL.ANY([]).[],fl_date=$0,op_unique_carrier=$1,tail_num=$2,op_carrier_fl_num=$3,origin_airport_id=$4,dest_airport_id=$5,crs_dep_time=$6,dep_time=$7,dep_delay=$8,taxi_out=$9,wheels_off=$10,wheels_on=$11,taxi_in=$12,arr_time=$13,arr_delay=$14,air_time=$15,distance=$16), >> > rowcount=8370632.0, cumulative cost={2.5111896E7 rows, 7.19874352E8 cpu, >> > 0.0 io, 0.0 network, 0.0 memory} Set#3, type: RecordType(ANY >> > dest_airport_id, INTEGER $f1) rel#18604:Subset#3.NONE.ANY([]).[], >> > best=null, importance=0.531441 >> > >> > >> rel#18634:LogicalProject.NONE.ANY([]).[](input=rel#18600:Subset#1.NONE.ANY([]).[],dest_airport_id=$5,$f1=1), >> > rowcount=100.0, cumulative cost={inf} >> > >> > >> rel#18638:LogicalProject.NONE.ANY([]).[](input=rel#18598:Subset#0.ENUMERABLE.ANY([]).[],dest_airport_id=ITEM($1, >> > 5),$f1=1), rowcount=100.0, cumulative cost={inf} >> > rel#18630:Subset#3.LOGICAL.ANY([]).[], best=rel#18643, >> importance=0.2657205 >> > >> > >> rel#18637:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18636:Subset#1.LOGICAL.ANY([]).[],dest_airport_id=$5,$f1=1), >> > rowcount=8370632.0, cumulative cost={2.5111896E7 rows, 6.19426768E8 cpu, >> > 0.0 io, 0.0 network, 0.0 memory} >> > >> > >> rel#18643:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18642:Subset#9.LOGICAL.ANY([]).[],dest_airport_id=ITEM($0, >> > 5),$f1=1), rowcount=8370632.0, cumulative cost={1.6741264E7 rows, >> > 7.5335688E7 cpu, 0.0 io, 0.0 network, 0.0 memory} >> > >> > >> rel#18645:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18644:Subset#0.LOGICAL.ANY([]).[],dest_airport_id=ITEM($1, >> > 5),$f1=1), rowcount=8370632.0, cumulative cost={1.6741264E7 rows, >> > 8.3773285056E10 cpu, 0.0 io, 0.0 network, 0.0 memory} Set#4, type: >> > RecordType(ANY dest_airport_id, BIGINT cnt) >> > rel#18606:Subset#4.NONE.ANY([]).[], best=null, >> > importance=0.5904900000000001 >> > >> > >> rel#18605:LogicalAggregate.NONE.ANY([]).[](input=rel#18604:Subset#3.NONE.ANY([]).[],group={0},cnt=COUNT($1)), >> > rowcount=10.0, cumulative cost={inf} >> rel#18628:Subset#4.LOGICAL.ANY([]).[], >> > best=rel#18631, importance=0.29524500000000004 >> > >> > >> rel#18631:DrillAggregateRel.LOGICAL.ANY([]).[](input=rel#18630:Subset#3.LOGICAL.ANY([]).[],group={0},cnt=COUNT($1)), >> > rowcount=8370581.997901573, cumulative cost={2.5111896E7 rows, >> 2.42748328E8 >> > cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 memory} Set#5, type: >> > RecordType(BIGINT cnt) rel#18608:Subset#5.NONE.ANY([]).[], best=null, >> > importance=0.6561 >> > >> > >> rel#18607:LogicalProject.NONE.ANY([]).[](input=rel#18606:Subset#4.NONE.ANY([]).[],cnt=$1), >> > rowcount=10.0, cumulative cost={inf} >> rel#18609:LogicalSort.NONE.ANY([]).[0 >> > DESC](input=rel#18608:Subset#5.NONE.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=10.0, cumulative cost={inf} >> rel#18621:Subset#5.LOGICAL.ANY([]).[], >> > best=rel#18629, importance=0.405 >> rel#18622:DrillSortRel.LOGICAL.ANY([]).[0 >> > DESC](input=rel#18621:Subset#5.LOGICAL.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=8370581.997901573, cumulative cost={4.185305999580315E7 rows, >> > 7.848350521732183E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > >> > >> rel#18629:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18628:Subset#4.LOGICAL.ANY([]).[],cnt=$1), >> > rowcount=8370581.997901573, cumulative cost={3.3482477997901574E7 rows, >> > 2.5111890999790156E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > rel#18624:Subset#5.NONE.ANY([]).[0 DESC], best=null, >> > importance=0.7290000000000001 rel#18609:LogicalSort.NONE.ANY([]).[0 >> > DESC](input=rel#18608:Subset#5.NONE.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=10.0, cumulative cost={inf} >> rel#18625:Subset#5.LOGICAL.ANY([]).[1 >> > DESC], best=null, importance=0.81 rel#18626:Subset#5.LOGICAL.ANY([]).[0 >> > DESC], best=rel#18622, importance=0.405 >> > rel#18622:DrillSortRel.LOGICAL.ANY([]).[0 >> > DESC](input=rel#18621:Subset#5.LOGICAL.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=8370581.997901573, cumulative cost={4.185305999580315E7 rows, >> > 7.848350521732183E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > Set#7, type: RecordType(BIGINT cnt, BIGINT w0$o0) >> > rel#18612:Subset#7.NONE.ANY([]).[1 DESC], best=null, importance=0.81 >> > rel#18611:LogicalWindow.NONE.ANY([]).[[1 >> > DESC]](input=rel#18624:Subset#5.NONE.ANY([]).[0 >> > DESC],window#0=window(partition {} order by [0 DESC] rows between >> UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])), rowcount=10.0, >> cumulative >> > cost={inf} rel#18617:Subset#7.LOGICAL.ANY([]).[1 DESC], best=null, >> > importance=0.9 rel#18620:DrillWindowRel.LOGICAL.ANY([]).[1 >> > DESC](input=rel#18625:Subset#5.LOGICAL.ANY([]).[1 >> > DESC],window#0=window(partition {} order by [0 DESC] rows between >> UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])), rowcount=10.0, >> cumulative >> > cost={inf} Set#8, type: RecordType(BIGINT r, BIGINT cnt) >> > rel#18614:Subset#8.NONE.ANY([]).[1 DESC], best=null, importance=0.9 >> > rel#18613:LogicalProject.NONE.ANY([]).[[1 >> > DESC]](input=rel#18612:Subset#7.NONE.ANY([]).[1 DESC],r=$1,cnt=$0), >> > rowcount=10.0, cumulative cost={inf} >> rel#18615:Subset#8.LOGICAL.ANY([]).[1 >> > DESC], best=null, importance=1.0 >> > rel#18616:AbstractConverter.LOGICAL.ANY([]).[1 >> > DESC](input=rel#18614:Subset#8.NONE.ANY([]).[1 >> > DESC],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[1 >> DESC]), >> > rowcount=10.0, cumulative cost={inf} >> > rel#18618:DrillProjectRel.LOGICAL.ANY([]).[[1 >> > DESC]](input=rel#18617:Subset#7.LOGICAL.ANY([]).[1 DESC],r=$1,cnt=$0), >> > rowcount=10.0, cumulative cost={inf} Set#9, type: RecordType(ANY >> columns) >> > rel#18642:Subset#9.LOGICAL.ANY([]).[], best=rel#18640, >> > importance=0.05314410000000001 >> > rel#18640:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, >> > columns=[`columns`[5]], >> > files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8370632.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory} Set#10, type: RecordType(ANY columns) >> > rel#18648:Subset#10.LOGICAL.ANY([]).[], best=rel#18646, >> > importance=0.007410233661971832 >> > rel#18646:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, >> > columns=[`columns`[0], `columns`[1], `columns`[2], `columns`[3], >> > `columns`[4], `columns`[5], `columns`[6], `columns`[7], `columns`[8], >> > `columns`[9], `columns`[10], `columns`[11], `columns`[12], >> `columns`[13], >> > `columns`[14], `columns`[15], `columns`[16]], >> > files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8370632.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory} [Error Id: e05bca25-7110-47aa-b872-60aa1b2fdc34 on >> > mdn-1.mdn.tdunning-dsr-demo-z6io48.svc.cluster.local:31010]. at >> > >> > >> com.mapr.drill.drill.dataengine.DRQryResultListener.checkAndThrowException(Unknown >> > Source) at >> > com.mapr.drill.drill.dataengine.DRQryResultListener.getNextBatch(Unknown >> > Source) at >> > >> > >> com.mapr.drill.drill.dataengine.DRJDBCResultSet.doLoadRecordBatchData(Unknown >> > Source) at >> > com.mapr.drill.drill.dataengine.DRJDBCResultSet.doMoveToNextRow(Unknown >> > Source) at >> > com.mapr.drill.drill.dataengine.DRJDBCQueryExecutor.execute(Unknown >> Source) >> > at com.mapr.drill.jdbc.common.SStatement.executeNoParams(Unknown >> Source) at >> > com.mapr.drill.jdbc.common.SStatement.execute(Unknown Source) at >> > >> > >> org.apache.commons.dbcp2.DelegatingStatement.execute(DelegatingStatement.java:291) >> > at >> > >> > >> org.apache.commons.dbcp2.DelegatingStatement.execute(DelegatingStatement.java:291) >> > at >> > >> > >> org.apache.zeppelin.jdbc.JDBCInterpreter.executeSql(JDBCInterpreter.java:718) >> > at >> > >> > >> org.apache.zeppelin.jdbc.JDBCInterpreter.interpret(JDBCInterpreter.java:801) >> > at >> > >> > >> org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:103) >> > at >> > >> > >> org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:633) >> > at org.apache.zeppelin.scheduler.Job.run(Job.java:188) at >> > >> > >> org.apache.zeppelin.scheduler.ParallelScheduler$JobRunner.run(ParallelScheduler.java:162) >> > at >> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) >> > at java.util.concurrent.FutureTask.run(FutureTask.java:266) at >> > >> > >> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180) >> > at >> > >> > >> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) >> > at >> > >> > >> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) >> > at >> > >> > >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) >> > Caused by: com.mapr.drill.support.exceptions.GeneralException: >> > [MapR][DrillJDBCDriver](500165) Query execution error. Details: SYSTEM >> > ERROR: CannotPlanException: Node [rel#18615:Subset#8.LOGICAL.ANY([]).[1 >> > DESC]] could not be implemented; planner state: Root: >> > rel#18615:Subset#8.LOGICAL.ANY([]).[1 DESC] Original rel: >> > LogicalProject(subset=[rel#18615:Subset#8.LOGICAL.ANY([]).[1 DESC]], >> > r=[$1], cnt=[$0]): rowcount = 10.0, cumulative cost = {10.0 rows, 20.0 >> cpu, >> > 0.0 io, 0.0 network, 0.0 memory}, id = 18613 >> > LogicalWindow(subset=[rel#18612:Subset#7.NONE.ANY([]).[1 DESC]], >> > window#0=[window(partition {} order by [0 DESC] rows between UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])]): rowcount = 10.0, >> > cumulative cost = {10.0 rows, 20.0 cpu, 0.0 io, 0.0 network, 0.0 >> memory}, >> > id = 18611 LogicalSort(subset=[rel#18610:Subset#6.NONE.ANY([]).[0 >> DESC]], >> > sort0=[$0], dir0=[DESC]): rowcount = 10.0, cumulative cost = {10.0 rows, >> > 92.10340371976184 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18609 >> > LogicalProject(subset=[rel#18608:Subset#5.NONE.ANY([]).[]], cnt=[$1]): >> > rowcount = 10.0, cumulative cost = {10.0 rows, 10.0 cpu, 0.0 io, 0.0 >> > network, 0.0 memory}, id = 18607 >> > LogicalAggregate(subset=[rel#18606:Subset#4.NONE.ANY([]).[]], >> group=[{0}], >> > cnt=[COUNT($1)]): rowcount = 10.0, cumulative cost = {11.25 rows, 0.0 >> cpu, >> > 0.0 io, 0.0 network, 0.0 memory}, id = 18605 >> > LogicalProject(subset=[rel#18604:Subset#3.NONE.ANY([]).[]], >> > dest_airport_id=[$5], $f1=[1]): rowcount = 100.0, cumulative cost = >> {100.0 >> > rows, 200.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18603 >> > LogicalProject(subset=[rel#18602:Subset#2.NONE.ANY([]).[]], >> fl_date=[$0], >> > op_unique_carrier=[$1], tail_num=[$2], op_carrier_fl_num=[$3], >> > origin_airport_id=[$4], dest_airport_id=[$5], crs_dep_time=[$6], >> > dep_time=[$7], dep_delay=[$8], taxi_out=[$9], wheels_off=[$10], >> > wheels_on=[$11], taxi_in=[$12], arr_time=[$13], arr_delay=[$14], >> > air_time=[$15], distance=[$16]): rowcount = 100.0, cumulative cost = >> {100.0 >> > rows, 1700.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18601 >> > LogicalProject(subset=[rel#18600:Subset#1.NONE.ANY([]).[]], >> > EXPR$0=[ITEM($1, 0)], EXPR$1=[ITEM($1, 1)], EXPR$2=[ITEM($1, 2)], >> > EXPR$3=[ITEM($1, 3)], EXPR$4=[ITEM($1, 4)], EXPR$5=[ITEM($1, 5)], >> > EXPR$6=[ITEM($1, 6)], EXPR$7=[ITEM($1, 7)], EXPR$8=[ITEM($1, 8)], >> > EXPR$9=[ITEM($1, 9)], EXPR$10=[ITEM($1, 10)], EXPR$11=[ITEM($1, 11)], >> > EXPR$12=[ITEM($1, 12)], EXPR$13=[ITEM($1, 13)], EXPR$14=[ITEM($1, 14)], >> > EXPR$15=[ITEM($1, 15)], EXPR$16=[ITEM($1, 16)]): rowcount = 100.0, >> > cumulative cost = {100.0 rows, 1700.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory}, id = 18599 >> > EnumerableTableScan(subset=[rel#18598:Subset#0.ENUMERABLE.ANY([]).[]], >> > table=[[dfs, flt, flights-2018-*.csv]]): rowcount = 100.0, cumulative >> cost >> > = {100.0 rows, 101.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 18507 >> > Sets: Set#0, type: RecordType(DYNAMIC_STAR **, ANY columns) >> > rel#18598:Subset#0.ENUMERABLE.ANY([]).[], best=rel#18507, >> > importance=0.4304672100000001 >> > rel#18507:EnumerableTableScan.ENUMERABLE.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv]), rowcount=100.0, cumulative cost={100.0 rows, 101.0 >> > cpu, 0.0 io, 0.0 network, 0.0 memory} >> > rel#18644:Subset#0.LOGICAL.ANY([]).[], best=rel#18653, >> > importance=0.28936962450000003 >> > rel#18653:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, columns=[`**`, >> > `columns`], files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8.370632E10 cpu, 0.0 io, 0.0 network, >> 0.0 >> > memory} Set#1, type: RecordType(ANY EXPR$0, ANY EXPR$1, ANY EXPR$2, ANY >> > EXPR$3, ANY EXPR$4, ANY EXPR$5, ANY EXPR$6, ANY EXPR$7, ANY EXPR$8, ANY >> > EXPR$9, ANY EXPR$10, ANY EXPR$11, ANY EXPR$12, ANY EXPR$13, ANY EXPR$14, >> > ANY EXPR$15, ANY EXPR$16) rel#18600:Subset#1.NONE.ANY([]).[], best=null, >> > importance=0.4782969000000001 >> > >> > >> rel#18599:LogicalProject.NONE.ANY([]).[](input=rel#18598:Subset#0.ENUMERABLE.ANY([]).[],EXPR$0=ITEM($1, >> > 0),EXPR$1=ITEM($1, 1),EXPR$2=ITEM($1, 2),EXPR$3=ITEM($1, >> 3),EXPR$4=ITEM($1, >> > 4),EXPR$5=ITEM($1, 5),EXPR$6=ITEM($1, 6),EXPR$7=ITEM($1, >> 7),EXPR$8=ITEM($1, >> > 8),EXPR$9=ITEM($1, 9),EXPR$10=ITEM($1, 10),EXPR$11=ITEM($1, >> > 11),EXPR$12=ITEM($1, 12),EXPR$13=ITEM($1, 13),EXPR$14=ITEM($1, >> > 14),EXPR$15=ITEM($1, 15),EXPR$16=ITEM($1, 16)), rowcount=100.0, >> cumulative >> > cost={inf} >> > >> > >> rel#18601:LogicalProject.NONE.ANY([]).[](input=rel#18600:Subset#1.NONE.ANY([]).[],fl_date=$0,op_unique_carrier=$1,tail_num=$2,op_carrier_fl_num=$3,origin_airport_id=$4,dest_airport_id=$5,crs_dep_time=$6,dep_time=$7,dep_delay=$8,taxi_out=$9,wheels_off=$10,wheels_on=$11,taxi_in=$12,arr_time=$13,arr_delay=$14,air_time=$15,distance=$16), >> > rowcount=100.0, cumulative cost={inf} >> > rel#18636:Subset#1.LOGICAL.ANY([]).[], best=rel#18649, >> > importance=0.28936962450000003 >> > >> > >> rel#18649:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18648:Subset#10.LOGICAL.ANY([]).[],EXPR$0=ITEM($0, >> > 0),EXPR$1=ITEM($0, 1),EXPR$2=ITEM($0, 2),EXPR$3=ITEM($0, >> 3),EXPR$4=ITEM($0, >> > 4),EXPR$5=ITEM($0, 5),EXPR$6=ITEM($0, 6),EXPR$7=ITEM($0, >> 7),EXPR$8=ITEM($0, >> > 8),EXPR$9=ITEM($0, 9),EXPR$10=ITEM($0, 10),EXPR$11=ITEM($0, >> > 11),EXPR$12=ITEM($0, 12),EXPR$13=ITEM($0, 13),EXPR$14=ITEM($0, >> > 14),EXPR$15=ITEM($0, 15),EXPR$16=ITEM($0, 16)), rowcount=8370632.0, >> > cumulative cost={1.6741264E7 rows, 5.77573608E8 cpu, 0.0 io, 0.0 >> network, >> > 0.0 memory} >> > >> > >> rel#18650:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18644:Subset#0.LOGICAL.ANY([]).[],EXPR$0=ITEM($1, >> > 0),EXPR$1=ITEM($1, 1),EXPR$2=ITEM($1, 2),EXPR$3=ITEM($1, >> 3),EXPR$4=ITEM($1, >> > 4),EXPR$5=ITEM($1, 5),EXPR$6=ITEM($1, 6),EXPR$7=ITEM($1, >> 7),EXPR$8=ITEM($1, >> > 8),EXPR$9=ITEM($1, 9),EXPR$10=ITEM($1, 10),EXPR$11=ITEM($1, >> > 11),EXPR$12=ITEM($1, 12),EXPR$13=ITEM($1, 13),EXPR$14=ITEM($1, >> > 14),EXPR$15=ITEM($1, 15),EXPR$16=ITEM($1, 16)), rowcount=8370632.0, >> > cumulative cost={1.6741264E7 rows, 8.4275522976E10 cpu, 0.0 io, 0.0 >> > network, 0.0 memory} >> > >> > >> rel#18651:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18636:Subset#1.LOGICAL.ANY([]).[],fl_date=$0,op_unique_carrier=$1,tail_num=$2,op_carrier_fl_num=$3,origin_airport_id=$4,dest_airport_id=$5,crs_dep_time=$6,dep_time=$7,dep_delay=$8,taxi_out=$9,wheels_off=$10,wheels_on=$11,taxi_in=$12,arr_time=$13,arr_delay=$14,air_time=$15,distance=$16), >> > rowcount=8370632.0, cumulative cost={2.5111896E7 rows, 7.19874352E8 cpu, >> > 0.0 io, 0.0 network, 0.0 memory} Set#3, type: RecordType(ANY >> > dest_airport_id, INTEGER $f1) rel#18604:Subset#3.NONE.ANY([]).[], >> > best=null, importance=0.531441 >> > >> > >> rel#18634:LogicalProject.NONE.ANY([]).[](input=rel#18600:Subset#1.NONE.ANY([]).[],dest_airport_id=$5,$f1=1), >> > rowcount=100.0, cumulative cost={inf} >> > >> > >> rel#18638:LogicalProject.NONE.ANY([]).[](input=rel#18598:Subset#0.ENUMERABLE.ANY([]).[],dest_airport_id=ITEM($1, >> > 5),$f1=1), rowcount=100.0, cumulative cost={inf} >> > rel#18630:Subset#3.LOGICAL.ANY([]).[], best=rel#18643, >> importance=0.2657205 >> > >> > >> rel#18637:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18636:Subset#1.LOGICAL.ANY([]).[],dest_airport_id=$5,$f1=1), >> > rowcount=8370632.0, cumulative cost={2.5111896E7 rows, 6.19426768E8 cpu, >> > 0.0 io, 0.0 network, 0.0 memory} >> > >> > >> rel#18643:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18642:Subset#9.LOGICAL.ANY([]).[],dest_airport_id=ITEM($0, >> > 5),$f1=1), rowcount=8370632.0, cumulative cost={1.6741264E7 rows, >> > 7.5335688E7 cpu, 0.0 io, 0.0 network, 0.0 memory} >> > >> > >> rel#18645:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18644:Subset#0.LOGICAL.ANY([]).[],dest_airport_id=ITEM($1, >> > 5),$f1=1), rowcount=8370632.0, cumulative cost={1.6741264E7 rows, >> > 8.3773285056E10 cpu, 0.0 io, 0.0 network, 0.0 memory} Set#4, type: >> > RecordType(ANY dest_airport_id, BIGINT cnt) >> > rel#18606:Subset#4.NONE.ANY([]).[], best=null, >> > importance=0.5904900000000001 >> > >> > >> rel#18605:LogicalAggregate.NONE.ANY([]).[](input=rel#18604:Subset#3.NONE.ANY([]).[],group={0},cnt=COUNT($1)), >> > rowcount=10.0, cumulative cost={inf} >> rel#18628:Subset#4.LOGICAL.ANY([]).[], >> > best=rel#18631, importance=0.29524500000000004 >> > >> > >> rel#18631:DrillAggregateRel.LOGICAL.ANY([]).[](input=rel#18630:Subset#3.LOGICAL.ANY([]).[],group={0},cnt=COUNT($1)), >> > rowcount=8370581.997901573, cumulative cost={2.5111896E7 rows, >> 2.42748328E8 >> > cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 memory} Set#5, type: >> > RecordType(BIGINT cnt) rel#18608:Subset#5.NONE.ANY([]).[], best=null, >> > importance=0.6561 >> > >> > >> rel#18607:LogicalProject.NONE.ANY([]).[](input=rel#18606:Subset#4.NONE.ANY([]).[],cnt=$1), >> > rowcount=10.0, cumulative cost={inf} >> rel#18609:LogicalSort.NONE.ANY([]).[0 >> > DESC](input=rel#18608:Subset#5.NONE.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=10.0, cumulative cost={inf} >> rel#18621:Subset#5.LOGICAL.ANY([]).[], >> > best=rel#18629, importance=0.405 >> rel#18622:DrillSortRel.LOGICAL.ANY([]).[0 >> > DESC](input=rel#18621:Subset#5.LOGICAL.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=8370581.997901573, cumulative cost={4.185305999580315E7 rows, >> > 7.848350521732183E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > >> > >> rel#18629:DrillProjectRel.LOGICAL.ANY([]).[](input=rel#18628:Subset#4.LOGICAL.ANY([]).[],cnt=$1), >> > rowcount=8370581.997901573, cumulative cost={3.3482477997901574E7 rows, >> > 2.5111890999790156E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > rel#18624:Subset#5.NONE.ANY([]).[0 DESC], best=null, >> > importance=0.7290000000000001 rel#18609:LogicalSort.NONE.ANY([]).[0 >> > DESC](input=rel#18608:Subset#5.NONE.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=10.0, cumulative cost={inf} >> rel#18625:Subset#5.LOGICAL.ANY([]).[1 >> > DESC], best=null, importance=0.81 rel#18626:Subset#5.LOGICAL.ANY([]).[0 >> > DESC], best=rel#18622, importance=0.405 >> > rel#18622:DrillSortRel.LOGICAL.ANY([]).[0 >> > DESC](input=rel#18621:Subset#5.LOGICAL.ANY([]).[],sort0=$0,dir0=DESC), >> > rowcount=8370581.997901573, cumulative cost={4.185305999580315E7 rows, >> > 7.848350521732183E8 cpu, 0.0 io, 0.0 network, 1.4732312320000002E8 >> memory} >> > Set#7, type: RecordType(BIGINT cnt, BIGINT w0$o0) >> > rel#18612:Subset#7.NONE.ANY([]).[1 DESC], best=null, importance=0.81 >> > rel#18611:LogicalWindow.NONE.ANY([]).[[1 >> > DESC]](input=rel#18624:Subset#5.NONE.ANY([]).[0 >> > DESC],window#0=window(partition {} order by [0 DESC] rows between >> UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])), rowcount=10.0, >> cumulative >> > cost={inf} rel#18617:Subset#7.LOGICAL.ANY([]).[1 DESC], best=null, >> > importance=0.9 rel#18620:DrillWindowRel.LOGICAL.ANY([]).[1 >> > DESC](input=rel#18625:Subset#5.LOGICAL.ANY([]).[1 >> > DESC],window#0=window(partition {} order by [0 DESC] rows between >> UNBOUNDED >> > PRECEDING and CURRENT ROW aggs [ROW_NUMBER()])), rowcount=10.0, >> cumulative >> > cost={inf} Set#8, type: RecordType(BIGINT r, BIGINT cnt) >> > rel#18614:Subset#8.NONE.ANY([]).[1 DESC], best=null, importance=0.9 >> > rel#18613:LogicalProject.NONE.ANY([]).[[1 >> > DESC]](input=rel#18612:Subset#7.NONE.ANY([]).[1 DESC],r=$1,cnt=$0), >> > rowcount=10.0, cumulative cost={inf} >> rel#18615:Subset#8.LOGICAL.ANY([]).[1 >> > DESC], best=null, importance=1.0 >> > rel#18616:AbstractConverter.LOGICAL.ANY([]).[1 >> > DESC](input=rel#18614:Subset#8.NONE.ANY([]).[1 >> > DESC],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[1 >> DESC]), >> > rowcount=10.0, cumulative cost={inf} >> > rel#18618:DrillProjectRel.LOGICAL.ANY([]).[[1 >> > DESC]](input=rel#18617:Subset#7.LOGICAL.ANY([]).[1 DESC],r=$1,cnt=$0), >> > rowcount=10.0, cumulative cost={inf} Set#9, type: RecordType(ANY >> columns) >> > rel#18642:Subset#9.LOGICAL.ANY([]).[], best=rel#18640, >> > importance=0.05314410000000001 >> > rel#18640:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, >> > columns=[`columns`[5]], >> > files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8370632.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory} Set#10, type: RecordType(ANY columns) >> > rel#18648:Subset#10.LOGICAL.ANY([]).[], best=rel#18646, >> > importance=0.007410233661971832 >> > rel#18646:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, flt, >> > flights-2018-*.csv],groupscan=EasyGroupScan >> > [selectionRoot=maprfs:/mapr/61-demo/flights, numFiles=12, >> > columns=[`columns`[0], `columns`[1], `columns`[2], `columns`[3], >> > `columns`[4], `columns`[5], `columns`[6], `columns`[7], `columns`[8], >> > `columns`[9], `columns`[10], `columns`[11], `columns`[12], >> `columns`[13], >> > `columns`[14], `columns`[15], `columns`[16]], >> > files=[maprfs:/mapr/61-demo/flights/flights-2018-01.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-02.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-03.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-04.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-05.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-06.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-07.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-08.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-09.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-10.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-11.csv, >> > maprfs:/mapr/61-demo/flights/flights-2018-12.csv]]), rowcount=8370632.0, >> > cumulative cost={8370632.0 rows, 8370632.0 cpu, 0.0 io, 0.0 network, 0.0 >> > memory} [Error Id: e05bca25-7110-47aa-b872-60aa1b2fdc34 on >> > mdn-1.mdn.tdunning-dsr-demo-z6io48.svc.cluster.local:31010]. ... 21 more >> > >> >
