[DRILL-786] Implement CROSS JOIN - ASF JIRA

Details

Type: New Feature
Status: Resolved
Priority: Major
Resolution: Fixed
Affects Version/s: 1.14.0
Fix Version/s: 1.15.0
Component/s: Query Planning & Optimization
Labels:
- doc-complete
- ready-to-commit

Description

For documentation:

Due to it's nature cross joins can produce extremely large results, and we don't recommend to use the feature if you don't know that results won't cause out of memory errors. That's why cross joins are disabled by default, to allow explicit cross join syntax you'll have to enable it by setting planner.enable_nljoin_for_scalar_only option to false. There is also another limitation related to usage of aggregation function over cross join relation. When input row count for aggregate function is bigger than value of planner.slice_target option then query can't be planned (because 2 phase aggregation can't be created in such case), as a workaround you should set planner.enable_multiphase_agg to false. This limitation will be active until fix of https://issues.apache.org/jira/browse/DRILL-6839.

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git.commit.id.abbrev=5d7e3d3

0: jdbc:drill:schema=dfs> select student.name, student.age, student.studentnum from student cross join voter where student.age = 20 and voter.age = 20;
Query failed: org.apache.drill.exec.rpc.RpcException: Remote failure while running query.[error_id: "af90e65a-c4d7-4635-a436-bbc1444c8db2"

Root: rel#318:Subset#28.PHYSICAL.SINGLETON([]).[]
Original rel:
AbstractConverter(subset=[rel#318:Subset#28.PHYSICAL.SINGLETON([]).[]], convention=[PHYSICAL], DrillDistributionTraitDef=[SINGLETON([])], sort=[[]]): rowcount = 22500.0, cumulative cost =

{inf}, id = 320
DrillScreenRel(subset=[rel#317:Subset#28.LOGICAL.ANY([]).[]]): rowcount = 22500.0, cumulative cost = {2250.0 rows, 2250.0 cpu, 0.0 io, 0.0 network}, id = 316
DrillProjectRel(subset=[rel#315:Subset#27.LOGICAL.ANY([]).[]], name=[$2], age=[$1], studentnum=[$3]): rowcount = 22500.0, cumulative cost = {22500.0 rows, 12.0 cpu, 0.0 io, 0.0 network}, id = 314
DrillJoinRel(subset=[rel#313:Subset#26.LOGICAL.ANY([]).[]], condition=[true], joinType=[inner]): rowcount = 22500.0, cumulative cost = {22500.0 rows, 0.0 cpu, 0.0 io, 0.0 network}, id = 312
DrillFilterRel(subset=[rel#308:Subset#23.LOGICAL.ANY([]).[]], condition=[=(CAST($1):INTEGER, 20)]): rowcount = 150.0, cumulative cost = {1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}, id = 307
DrillScanRel(subset=[rel#306:Subset#22.LOGICAL.ANY([]).[]], table=[[dfs, student]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}, id = 129
DrillFilterRel(subset=[rel#311:Subset#25.LOGICAL.ANY([]).[]], condition=[=(CAST($1):INTEGER, 20)]): rowcount = 150.0, cumulative cost = {1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}, id = 310
DrillScanRel(subset=[rel#309:Subset#24.LOGICAL.ANY([]).[]], table=[[dfs, voter]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 2000.0 cpu, 0.0 io, 0.0 network}, id = 140

Stack trace:
org.eigenbase.relopt.RelOptPlanner$CannotPlanException: Node [rel#318:Subset#28.PHYSICAL.SINGLETON([]).[]] could not be implemented; planner state:

Root: rel#318:Subset#28.PHYSICAL.SINGLETON([]).[]
Original rel:
AbstractConverter(subset=[rel#318:Subset#28.PHYSICAL.SINGLETON([]).[]], convention=[PHYSICAL], DrillDistributionTraitDef=[SINGLETON([])], sort=[[]]): rowcount = 22500.0, cumulative cost = {inf}

, id = 320
DrillScreenRel(subset=[rel#317:Subset#28.LOGICAL.ANY([]).[]]): rowcount = 22500.0, cumulative cost =

{2250.0 rows, 2250.0 cpu, 0.0 io, 0.0 network}

, id = 316
DrillProjectRel(subset=[rel#315:Subset#27.LOGICAL.ANY([]).[]], name=[$2], age=[$1], studentnum=[$3]): rowcount = 22500.0, cumulative cost =

{22500.0 rows, 12.0 cpu, 0.0 io, 0.0 network}

, id = 314
DrillJoinRel(subset=[rel#313:Subset#26.LOGICAL.ANY([]).[]], condition=[true], joinType=[inner]): rowcount = 22500.0, cumulative cost =

{22500.0 rows, 0.0 cpu, 0.0 io, 0.0 network}

, id = 312
DrillFilterRel(subset=[rel#308:Subset#23.LOGICAL.ANY([]).[]], condition=[=(CAST($1):INTEGER, 20)]): rowcount = 150.0, cumulative cost =

{1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}, id = 307
DrillScanRel(subset=[rel#306:Subset#22.LOGICAL.ANY([]).[]], table=[[dfs, student]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}

, id = 129
DrillFilterRel(subset=[rel#311:Subset#25.LOGICAL.ANY([]).[]], condition=[=(CAST($1):INTEGER, 20)]): rowcount = 150.0, cumulative cost =

{1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}, id = 310
DrillScanRel(subset=[rel#309:Subset#24.LOGICAL.ANY([]).[]], table=[[dfs, voter]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 2000.0 cpu, 0.0 io, 0.0 network}, id = 140

Sets:
Set#22, type: (DrillRecordRow[*, age, name, studentnum])
rel#306:Subset#22.LOGICAL.ANY([]).[], best=rel#129, importance=0.5904900000000001
rel#129:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, student]), rowcount=1000.0, cumulative cost={1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}

rel#333:AbstractConverter.LOGICAL.ANY([]).[](child=rel#332:Subset#22.PHYSICAL.ANY([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost=

{inf}
rel#337:AbstractConverter.LOGICAL.ANY([]).[](child=rel#336:Subset#22.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}

rel#332:Subset#22.PHYSICAL.ANY([]).[], best=rel#335, importance=0.531441
rel#334:AbstractConverter.PHYSICAL.ANY([]).[](child=rel#306:Subset#22.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost=

{inf}
rel#338:AbstractConverter.PHYSICAL.ANY([]).[](child=rel#336:Subset#22.PHYSICAL.SINGLETON([]).[],convention=PHYSICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}

rel#339:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#306:Subset#22.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost=

{inf}
rel#340:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#332:Subset#22.PHYSICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}

rel#335:ScanPrel.PHYSICAL.SINGLETON([]).[](groupscan=ParquetGroupScan [entries=[ReadEntryWithPath [path=maprfs:/drill/testdata/p1tests/student]], selectionRoot=/drill/testdata/p1tests/student, columns=[SchemaPath [`age`], SchemaPath [`name`], SchemaPath [`studentnum`]]]), rowcount=1000.0, cumulative cost=

{1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}
rel#336:Subset#22.PHYSICAL.SINGLETON([]).[], best=rel#335, importance=0.4782969000000001
rel#339:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#306:Subset#22.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#340:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#332:Subset#22.PHYSICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#335:ScanPrel.PHYSICAL.SINGLETON([]).[](groupscan=ParquetGroupScan [entries=[ReadEntryWithPath [path=maprfs:/drill/testdata/p1tests/student]], selectionRoot=/drill/testdata/p1tests/student, columns=[SchemaPath [`age`], SchemaPath [`name`], SchemaPath [`studentnum`]]]), rowcount=1000.0, cumulative cost={1000.0 rows, 4000.0 cpu, 0.0 io, 0.0 network}

Set#23, type: (DrillRecordRow[*, age, name, studentnum])
rel#308:Subset#23.LOGICAL.ANY([]).[], best=rel#307, importance=0.6561
rel#307:DrillFilterRel.LOGICAL.ANY([]).[](child=rel#306:Subset#22.LOGICAL.ANY([]).[],condition==(CAST($1):INTEGER, 20)), rowcount=150.0, cumulative cost=

{2000.0 rows, 8000.0 cpu, 0.0 io, 0.0 network}
rel#343:AbstractConverter.LOGICAL.ANY([]).[](child=rel#342:Subset#23.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=150.0, cumulative cost={inf}
rel#342:Subset#23.PHYSICAL.SINGLETON([]).[], best=rel#341, importance=0.5904900000000001
rel#344:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#308:Subset#23.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=150.0, cumulative cost={inf}
rel#341:FilterPrel.PHYSICAL.SINGLETON([]).[](child=rel#332:Subset#22.PHYSICAL.ANY([]).[],condition==(CAST($1):INTEGER, 20)), rowcount=150.0, cumulative cost={2000.0 rows, 8000.0 cpu, 0.0 io, 0.0 network}

Set#24, type: (DrillRecordRow[*, age])
rel#309:Subset#24.LOGICAL.ANY([]).[], best=rel#140, importance=0.5904900000000001
rel#140:DrillScanRel.LOGICAL.ANY([]).[](table=[dfs, voter]), rowcount=1000.0, cumulative cost=

{1000.0 rows, 2000.0 cpu, 0.0 io, 0.0 network}
rel#330:AbstractConverter.LOGICAL.ANY([]).[](child=rel#329:Subset#24.PHYSICAL.ANY([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#349:AbstractConverter.LOGICAL.ANY([]).[](child=rel#348:Subset#24.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#329:Subset#24.PHYSICAL.ANY([]).[], best=rel#347, importance=0.531441
rel#331:AbstractConverter.PHYSICAL.ANY([]).[](child=rel#309:Subset#24.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#350:AbstractConverter.PHYSICAL.ANY([]).[](child=rel#348:Subset#24.PHYSICAL.SINGLETON([]).[],convention=PHYSICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#351:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#309:Subset#24.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#352:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#329:Subset#24.PHYSICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}
rel#347:ScanPrel.PHYSICAL.SINGLETON([]).[](groupscan=ParquetGroupScan [entries=[ReadEntryWithPath [path=maprfs:/drill/testdata/p1tests/voter]], selectionRoot=/drill/testdata/p1tests/voter, columns=[SchemaPath [`age`]]]), rowcount=1000.0, cumulative cost={1000.0 rows, 2000.0 cpu, 0.0 io, 0.0 network}

rel#348:Subset#24.PHYSICAL.SINGLETON([]).[], best=rel#347, importance=0.4782969000000001
rel#351:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#309:Subset#24.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost=

{inf}
rel#352:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#329:Subset#24.PHYSICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=1000.0, cumulative cost={inf}

rel#347:ScanPrel.PHYSICAL.SINGLETON([]).[](groupscan=ParquetGroupScan [entries=[ReadEntryWithPath [path=maprfs:/drill/testdata/p1tests/voter]], selectionRoot=/drill/testdata/p1tests/voter, columns=[SchemaPath [`age`]]]), rowcount=1000.0, cumulative cost=

{1000.0 rows, 2000.0 cpu, 0.0 io, 0.0 network}

Set#25, type: (DrillRecordRow[*, age])
rel#311:Subset#25.LOGICAL.ANY([]).[], best=rel#310, importance=0.6561
rel#310:DrillFilterRel.LOGICAL.ANY([]).[](child=rel#309:Subset#24.LOGICAL.ANY([]).[],condition==(CAST($1):INTEGER, 20)), rowcount=150.0, cumulative cost=

{2000.0 rows, 6000.0 cpu, 0.0 io, 0.0 network}
rel#355:AbstractConverter.LOGICAL.ANY([]).[](child=rel#354:Subset#25.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=150.0, cumulative cost={inf}
rel#354:Subset#25.PHYSICAL.SINGLETON([]).[], best=rel#353, importance=0.5904900000000001
rel#356:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#311:Subset#25.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=150.0, cumulative cost={inf}
rel#353:FilterPrel.PHYSICAL.SINGLETON([]).[](child=rel#329:Subset#24.PHYSICAL.ANY([]).[],condition==(CAST($1):INTEGER, 20)), rowcount=150.0, cumulative cost={2000.0 rows, 6000.0 cpu, 0.0 io, 0.0 network}

Set#26, type: RecordType(ANY *, ANY age, ANY name, ANY studentnum, ANY *0, ANY age0)
rel#313:Subset#26.LOGICAL.ANY([]).[], best=rel#312, importance=0.7290000000000001
rel#312:DrillJoinRel.LOGICAL.ANY([]).[](left=rel#308:Subset#23.LOGICAL.ANY([]).[],right=rel#311:Subset#25.LOGICAL.ANY([]).[],condition=true,joinType=inner), rowcount=22500.0, cumulative cost=

{4001.0 rows, 14001.0 cpu, 0.0 io, 0.0 network}

rel#327:AbstractConverter.LOGICAL.ANY([]).[](child=rel#326:Subset#26.PHYSICAL.ANY([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1.7976931348623157E308, cumulative cost=

{inf}
rel#326:Subset#26.PHYSICAL.ANY([]).[], best=null, importance=0.6561
rel#328:AbstractConverter.PHYSICAL.ANY([]).[](child=rel#313:Subset#26.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=22500.0, cumulative cost={inf}

Set#27, type: RecordType(ANY name, ANY age, ANY studentnum)
rel#315:Subset#27.LOGICAL.ANY([]).[], best=rel#314, importance=0.81
rel#314:DrillProjectRel.LOGICAL.ANY([]).[](child=rel#313:Subset#26.LOGICAL.ANY([]).[],name=$2,age=$1,studentnum=$3), rowcount=22500.0, cumulative cost=

{26501.0 rows, 14013.0 cpu, 0.0 io, 0.0 network}

rel#322:AbstractConverter.LOGICAL.ANY([]).[](child=rel#321:Subset#27.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1.7976931348623157E308, cumulative cost=

{inf}
rel#321:Subset#27.PHYSICAL.SINGLETON([]).[], best=null, importance=0.7290000000000001
rel#323:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#315:Subset#27.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=22500.0, cumulative cost={inf}

Set#28, type: RecordType(ANY name, ANY age, ANY studentnum)
rel#317:Subset#28.LOGICAL.ANY([]).[], best=rel#316, importance=0.9
rel#316:DrillScreenRel.LOGICAL.ANY([]).[](child=rel#315:Subset#27.LOGICAL.ANY([]).[]), rowcount=22500.0, cumulative cost=

{28751.0 rows, 16263.0 cpu, 0.0 io, 0.0 network}

rel#319:AbstractConverter.LOGICAL.ANY([]).[](child=rel#318:Subset#28.PHYSICAL.SINGLETON([]).[],convention=LOGICAL,DrillDistributionTraitDef=ANY([]),sort=[]), rowcount=1.7976931348623157E308, cumulative cost=

{inf}
rel#318:Subset#28.PHYSICAL.SINGLETON([]).[], best=null, importance=1.0
rel#320:AbstractConverter.PHYSICAL.SINGLETON([]).[](child=rel#317:Subset#28.LOGICAL.ANY([]).[],convention=PHYSICAL,DrillDistributionTraitDef=SINGLETON([]),sort=[]), rowcount=22500.0, cumulative cost={inf}

rel#324:ScreenPrel.PHYSICAL.SINGLETON([]).[](child=rel#321:Subset#27.PHYSICAL.SINGLETON([]).[]), rowcount=1.7976931348623157E308, cumulative cost=

{inf}

org.eigenbase.relopt.volcano.RelSubset$CheapestPlanReplacer.visit(RelSubset.java:445) ~[optiq-core-0.7-20140513.013236-5.jar:na]
org.eigenbase.relopt.volcano.RelSubset.buildCheapestPlan(RelSubset.java:287) ~[optiq-core-0.7-20140513.013236-5.jar:na]
org.eigenbase.relopt.volcano.VolcanoPlanner.findBestExp(VolcanoPlanner.java:669) ~[optiq-core-0.7-20140513.013236-5.jar:na]
net.hydromatic.optiq.prepare.PlannerImpl.transform(PlannerImpl.java:271) ~[optiq-core-0.7-20140513.013236-5.jar:na]
org.apache.drill.exec.planner.sql.handlers.DefaultSqlHandler.convertToPrel(DefaultSqlHandler.java:119) ~[drill-java-exec-1.0.0-m2-incubating-SNAPSHOT-rebuffed.jar:1.0.0-m2-incubating-SNAPSHOT]
org.apache.drill.exec.planner.sql.handlers.DefaultSqlHandler.getPlan(DefaultSqlHandler.java:89) ~[drill-java-exec-1.0.0-m2-incubating-SNAPSHOT-rebuffed.jar:1.0.0-m2-incubating-SNAPSHOT]
org.apache.drill.exec.planner.sql.DrillSqlWorker.getPlan(DrillSqlWorker.java:134) ~[drill-java-exec-1.0.0-m2-incubating-SNAPSHOT-rebuffed.jar:1.0.0-m2-incubating-SNAPSHOT]
org.apache.drill.exec.work.foreman.Foreman.runSQL(Foreman.java:338) [drill-java-exec-1.0.0-m2-incubating-SNAPSHOT-rebuffed.jar:1.0.0-m2-incubating-SNAPSHOT]
org.apache.drill.exec.work.foreman.Foreman.run(Foreman.java:186) [drill-java-exec-1.0.0-m2-incubating-SNAPSHOT-rebuffed.jar:1.0.0-m2-incubating-SNAPSHOT]
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) [na:1.7.0_45]
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) [na:1.7.0_45]
java.lang.Thread.run(Thread.java:744) [na:1.7.0_45]