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Posted to issues@drill.apache.org by "Ihor Huzenko (JIRA)" <ji...@apache.org> on 2018/09/27 16:46:00 UTC
[jira] [Commented] (DRILL-786) Implement CROSS JOIN
[ https://issues.apache.org/jira/browse/DRILL-786?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16630719#comment-16630719 ]
Ihor Huzenko commented on DRILL-786:
------------------------------------
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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