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Posted to issues@drill.apache.org by "Igor Guzenko (JIRA)" <ji...@apache.org> on 2018/10/02 11:46:00 UTC
[jira] [Comment Edited] (DRILL-786) Implement CROSS JOIN
[ https://issues.apache.org/jira/browse/DRILL-786?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16635306#comment-16635306 ]
Igor Guzenko edited comment on DRILL-786 at 10/2/18 11:45 AM:
--------------------------------------------------------------
We considered 3 possible options how the feature could be implemented. Note, in text below when I mention option is enabled or disabled it relates to *planner.enable_nljoin_for_scalar_only* option.
*Option 1. (Perfect case) :*
Allow nested loop only for nodes that originated from explicit cross join syntax but prohibit implicit cross joins when option is enabled. So such query should fail when option is true:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b CROSS JOIN cp.`tpch/nation.parquet` c
{code}
Because cross join of *a* and result of (*b* x *c*) is implicit and should depend on option value. But based on my investigation, *{color:#d04437}I didn't find how this could be implemented{color}*{color:#d04437}.{color}
*Option 2. (Allow all queries with explicit cross join syntax)*
We can allow nested loop join for all queries that contain explicit cross join syntax regardless of option value. For example following queries will work in such case:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` l CROSS JOIN cp.`tpch/nation.parquet` r
{code}
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b CROSS JOIN cp.`tpch/nation.parquet` c
{code}
But queries that don't contain explicit syntax, will still be dependent on the option. For example the following query won't work when option is enabled:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b
{code}
*Option 3. (Allow cross join syntax only when option enabled)*
This approach is just more narrow case of the previous one. We could allow explicit cross join for enabled option, and prohibit it for disabled option.
was (Author: ihorhuzenko):
We considered 3 possible options how the feature could be implemented. Note, in text below when I mention option is enabled or disabled it relates to *planner.enable_nljoin_for_scalar_only* option.
*Option 1. (Perfect case) :*
Allow nested loop only for nodes that originated from explicit cross join syntax but prohibit implicit cross joins when option is enabled. So such query should fail when option is true:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b CROSS JOIN cp.`tpch/nation.parquet` c
{code}
Because cross join of *a* and result of (*b* x *c*) is implicit and should depend on option value. But based on my investigation, {color:#d04437}it's really hard to implement this approach, it requires a lot of time and includes a lot of changes to Apache Calcite.{color}
*Option 2. (Allow all queries with explicit cross join syntax)*
We can allow nested loop join for all queries that contain explicit cross join syntax regardless of option value. For example following queries will work in such case:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` l CROSS JOIN cp.`tpch/nation.parquet` r
{code}
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b CROSS JOIN cp.`tpch/nation.parquet` c
{code}
But queries that don't contain explicit syntax, will still be dependent on the option. For example the following query won't work when option is enabled:
{code:java}
SELECT *
FROM cp.`tpch/nation.parquet` a, cp.`tpch/nation.parquet` b
{code}
*Option 3. (Allow cross join syntax only when option enabled)*
This approach is just more narrow case of the previous one. We could allow explicit cross join for enabled option, and prohibit it for disabled option.
> 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: Igor Guzenko
> Priority: Major
> Fix For: 1.15.0
>
>
> 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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