[
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 11:53 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:#333333} I
have provided results of the investigation in the prior comments.{color}{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.
Also we can consider changing default value of the option to false thus
queries producing Cartesian product would always succeed.
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}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.
Also we can consider changing default value of the option to false thus
queries producing Cartesian product would always succeed.
> 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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