[
https://issues.apache.org/jira/browse/DRILL-786?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16630719#comment-16630719
]
Ihor Huzenko edited comment on DRILL-786 at 9/27/18 4:55 PM:
-------------------------------------------------------------
As you may know cross joins are allowed when option
{color:#59afe1}planner.enable_nljoin_for_scalar_only{color} is set to false and
join performed using comma syntax. But main goal of this task is to allow
explicit cross joins in queries when option is enabled and at the same time
disallow other ways to execute cross joins (for example, list tables via comma
in FROM section of query without condition) while option is enabled.
The main idea about how we could implement this task is to allow usage of
NestedLoopJoin in two places where mentioned above option is checked:
#
[DrillJoinRelBase.java|https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/planner/common/DrillJoinRelBase.java]
method *computeSelfCost*, line: _if
(PrelUtil.getPlannerSettings(planner).isNlJoinForScalarOnly())_
# [NestedLoopJoinPrule.java
|https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/planner/physical/NestedLoopJoinPrule.java]
method *checkPreconditions* line: _if (settings.isNlJoinForScalarOnly())._
But we should allow it only if our current join rel node is actually originated
from explicit cross join in SQL query. Here is where the main challenge comes
in, at this points we don't know that the rel node is explicit cross join.
Because after invocation of Calcite's
org.apache.calcite.sql2rel.SqlToRelConverter, converted SqlNode becomes
LogicalJoin rel node with type INNER, see SqlToRelConverter:
{code:java}
private static JoinRelType convertJoinType(JoinType joinType) {
switch (joinType) {
case COMMA:
case INNER:
case CROSS:
return JoinRelType.INNER;
case FULL:
return JoinRelType.FULL;
case LEFT:
return JoinRelType.LEFT;
case RIGHT:
return JoinRelType.RIGHT;
default:
throw Util.unexpected(joinType);
}
}
{code}
I tried to add custom RelTrait and with help of reflections magic I was even
able to overcome HepPlanner's conversions of LogicalJoin nodes. But then I got
an error from VolcanoPlanner's code:
{code:java}
if (traits.size() != traitDefs.size()) {
throw new AssertionError("Relational expression " + rel
+ " does not have the correct number of traits: " + traits.size()
+ " != " + traitDefs.size());
}
{code}
So it's impossible to use traitSet for marking that rel node is came from
explicit CROSS JOIN syntax. I see two options how we could overcome this
problem and both of them include changes to Calcite's LogicalJoin class (just
because it's final class :():
1) Either add additional flag and preserve it between recreation of LogicalJoin
instances, as it was done for field:
{code:java}
private final boolean semiJoinDone;
{code}
But major disadvantage of this approach is that updated constructors will break
other clients code.
2) Add ability to register static callback function that will be called after
creation of new instance inside copy method, and accept both oldRelNode and
newRelNode. So then we could trace ids of LogicalJoin instances since creation
of first such instance in org.apache.calcite.sql2rel.SqlToRelConverter.
This are all ideas that I have now. I'm very new to Drill and Calcite and maybe
I don't see other good alternatives. Dear drillers, could you please take a
look and share your thoughts about possible options ?
was (Author: ihorhuzenko):
As you may know CROSS JOIN syntax works fine when option
{color:#59afe1}planner.enable_nljoin_for_scalar_only{color} is set to false.
But main goal of this task is to allow explicit cross joins in queries when
option is enabled and at the same time disallow other ways to execute cross
joins (for example, list tables via comma in FROM section of query without
condition) while option is enabled.
The main idea about how we could implement this task is to allow usage of
NestedLoopJoin in two places where mentioned above option is checked:
#
[DrillJoinRelBase.java|https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/planner/common/DrillJoinRelBase.java]
method *computeSelfCost*, line: _if
(PrelUtil.getPlannerSettings(planner).isNlJoinForScalarOnly())_
# [NestedLoopJoinPrule.java
|https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/planner/physical/NestedLoopJoinPrule.java]
method *checkPreconditions* line: _if (settings.isNlJoinForScalarOnly())._
But we should allow it only if our current join rel node is actually originated
from explicit cross join in SQL query. Here is where the main challenge comes
in, at this points we don't know that the rel node is explicit cross join.
Because after invocation of Calcite's
org.apache.calcite.sql2rel.SqlToRelConverter, converted SqlNode becomes
LogicalJoin rel node with type INNER, see SqlToRelConverter:
{code:java}
private static JoinRelType convertJoinType(JoinType joinType) {
switch (joinType) {
case COMMA:
case INNER:
case CROSS:
return JoinRelType.INNER;
case FULL:
return JoinRelType.FULL;
case LEFT:
return JoinRelType.LEFT;
case RIGHT:
return JoinRelType.RIGHT;
default:
throw Util.unexpected(joinType);
}
}
{code}
I tried to add custom RelTrait and with help of reflections magic I was even
able to overcome HepPlanner's conversions of LogicalJoin nodes. But then I got
an error from VolcanoPlanner's code:
{code:java}
if (traits.size() != traitDefs.size()) {
throw new AssertionError("Relational expression " + rel
+ " does not have the correct number of traits: " + traits.size()
+ " != " + traitDefs.size());
}
{code}
So it's impossible to use traitSet for marking that rel node is came from
explicit CROSS JOIN syntax. I see two options how we could overcome this
problem and both of them include changes to Calcite's LogicalJoin class (just
because it's final class :():
1) Either add additional flag and preserve it between recreation of LogicalJoin
instances, as it was done for field:
{code:java}
private final boolean semiJoinDone;
{code}
But major disadvantage of this approach is that updated constructors will break
other clients code.
2) Add ability to register static callback function that will be called after
creation of new instance inside copy method, and accept both oldRelNode and
newRelNode. So then we could trace ids of LogicalJoin instances since creation
of first such instance in org.apache.calcite.sql2rel.SqlToRelConverter.
This are all ideas that I have now. I'm very new to Drill and Calcite and maybe
I don't see other good alternatives. Dear drillers, could you please take a
look and share your thoughts about possible options ?
> Implement CROSS JOIN
> --------------------
>
> Key: DRILL-786
> URL: https://issues.apache.org/jira/browse/DRILL-786
> Project: Apache Drill
> Issue Type: New Feature
> Components: Query Planning & Optimization
> Reporter: Krystal
> Assignee: Ihor Huzenko
> Priority: Major
> Fix For: Future
>
>
> 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]
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