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Posted to dev@flink.apache.org by "xiaojin.wy (Jira)" <ji...@apache.org> on 2021/09/07 11:34:00 UTC
[jira] [Created] (FLINK-24192) Sql get plan failed. All the inputs
have relevant nodes, however the cost is still infinite
xiaojin.wy created FLINK-24192:
----------------------------------
Summary: Sql get plan failed. All the inputs have relevant nodes, however the cost is still infinite
Key: FLINK-24192
URL: https://issues.apache.org/jira/browse/FLINK-24192
Project: Flink
Issue Type: Bug
Components: Table SQL / Planner
Affects Versions: 1.15.0
Reporter: xiaojin.wy
Fix For: 1.15.0
*sql*
{code:java}
CREATE TABLE database5_t0(
`c0` FLOAT , `c1` FLOAT , `c2` CHAR
) WITH (
'connector' = 'filesystem',
'format' = 'testcsv',
'path' = '$resultPath00'
)
CREATE TABLE database5_t1(
`c0` TINYINT , `c1` INTEGER
) WITH (
'connector' = 'filesystem',
'format' = 'testcsv',
'path' = '$resultPath11'
)
CREATE TABLE database5_t2 (
`c0` FLOAT
) WITH (
'connector' = 'filesystem',
'format' = 'testcsv',
'path' = '$resultPath33'
)
CREATE TABLE database5_t3 (
`c0` STRING , `c1` STRING
) WITH (
'connector' = 'filesystem',
'format' = 'testcsv',
'path' = '$resultPath33'
)
INSERT INTO database5_t0(c0, c1, c2) VALUES(cast(0.84355265 as FLOAT), cast(0.3269016 as FLOAT), cast('' as CHAR))
INSERT INTO database5_t1(c0, c1) VALUES(cast(-125 as TINYINT), -1715936454)
INSERT INTO database5_t2(c0) VALUES(cast(-1.7159365 as FLOAT))
INSERT INTO database5_t3(c0, c1) VALUES('16:36:29', '1969-12-12')
INSERT INTO MySink
SELECT COUNT(ref0) from (SELECT COUNT(1) AS ref0 FROM database5_t0, database5_t3, database5_t1, database5_t2 WHERE CAST ( EXISTS (SELECT 1) AS BOOLEAN)
UNION ALL
SELECT COUNT(1) AS ref0 FROM database5_t0, database5_t3, database5_t1, database5_t2
WHERE CAST ((NOT CAST (( EXISTS (SELECT 1)) AS BOOLEAN)) AS BOOLEAN)
UNION ALL
SELECT COUNT(1) AS ref0 FROM database5_t0, database5_t3, database5_t1, database5_t2 WHERE CAST ((CAST ( EXISTS (SELECT 1) AS BOOLEAN)) IS NULL AS BOOLEAN)) as table1
{code}
After excite the sql in it case, we get the error like this:
{code:java}
org.apache.flink.table.api.TableException: Cannot generate a valid execution plan for the given query:
FlinkLogicalSink(table=[default_catalog.default_database.MySink], fields=[a])
+- FlinkLogicalCalc(select=[CAST(EXPR$0) AS a])
+- FlinkLogicalAggregate(group=[{}], EXPR$0=[COUNT()])
+- FlinkLogicalUnion(all=[true])
:- FlinkLogicalUnion(all=[true])
: :- FlinkLogicalCalc(select=[0 AS $f0])
: : +- FlinkLogicalAggregate(group=[{}], ref0=[COUNT()])
: : +- FlinkLogicalJoin(condition=[$1], joinType=[semi])
: : :- FlinkLogicalCalc(select=[c0])
: : : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : : :- FlinkLogicalCalc(select=[c0])
: : : : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : : : :- FlinkLogicalCalc(select=[c0])
: : : : : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : : : : :- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t0, project=[c0]]], fields=[c0])
: : : : : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t3, project=[c0]]], fields=[c0])
: : : : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t1, project=[c0]]], fields=[c0])
: : : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t2]], fields=[c0])
: : +- FlinkLogicalCalc(select=[IS NOT NULL(m) AS $f0])
: : +- FlinkLogicalAggregate(group=[{}], m=[MIN($0)])
: : +- FlinkLogicalCalc(select=[true AS i])
: : +- FlinkLogicalValues(tuples=[[{ 0 }]])
: +- FlinkLogicalCalc(select=[0 AS $f0])
: +- FlinkLogicalAggregate(group=[{}], ref0=[COUNT()])
: +- FlinkLogicalJoin(condition=[$1], joinType=[anti])
: :- FlinkLogicalCalc(select=[c0])
: : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : :- FlinkLogicalCalc(select=[c0])
: : : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : : :- FlinkLogicalCalc(select=[c0])
: : : : +- FlinkLogicalJoin(condition=[true], joinType=[inner])
: : : : :- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t0, project=[c0]]], fields=[c0])
: : : : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t3, project=[c0]]], fields=[c0])
: : : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t1, project=[c0]]], fields=[c0])
: : +- FlinkLogicalTableSourceScan(table=[[default_catalog, default_database, database5_t2]], fields=[c0])
: +- FlinkLogicalCalc(select=[IS NOT NULL(m) AS $f0])
: +- FlinkLogicalAggregate(group=[{}], m=[MIN($0)])
: +- FlinkLogicalCalc(select=[true AS i])
: +- FlinkLogicalValues(tuples=[[{ 0 }]])
+- FlinkLogicalCalc(select=[0 AS $f0])
+- FlinkLogicalAggregate(group=[{}], ref0=[COUNT()])
+- FlinkLogicalValues(tuples=[[]])
This exception indicates that the query uses an unsupported SQL feature.
Please check the documentation for the set of currently supported SQL features.
at org.apache.flink.table.planner.plan.optimize.program.FlinkVolcanoProgram.optimize(FlinkVolcanoProgram.scala:72)
at org.apache.flink.table.planner.plan.optimize.program.FlinkChainedProgram$$anonfun$optimize$1.apply(FlinkChainedProgram.scala:62)
at org.apache.flink.table.planner.plan.optimize.program.FlinkChainedProgram$$anonfun$optimize$1.apply(FlinkChainedProgram.scala:58)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractTraversable.foldLeft(Traversable.scala:104)
at org.apache.flink.table.planner.plan.optimize.program.FlinkChainedProgram.optimize(FlinkChainedProgram.scala:57)
at org.apache.flink.table.planner.plan.optimize.BatchCommonSubGraphBasedOptimizer.optimizeTree(BatchCommonSubGraphBasedOptimizer.scala:87)
at org.apache.flink.table.planner.plan.optimize.BatchCommonSubGraphBasedOptimizer.org$apache$flink$table$planner$plan$optimize$BatchCommonSubGraphBasedOptimizer$$optimizeBlock(BatchCommonSubGraphBasedOptimizer.scala:58)
at org.apache.flink.table.planner.plan.optimize.BatchCommonSubGraphBasedOptimizer$$anonfun$doOptimize$1.apply(BatchCommonSubGraphBasedOptimizer.scala:46)
at org.apache.flink.table.planner.plan.optimize.BatchCommonSubGraphBasedOptimizer$$anonfun$doOptimize$1.apply(BatchCommonSubGraphBasedOptimizer.scala:46)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.flink.table.planner.plan.optimize.BatchCommonSubGraphBasedOptimizer.doOptimize(BatchCommonSubGraphBasedOptimizer.scala:46)
at org.apache.flink.table.planner.plan.optimize.CommonSubGraphBasedOptimizer.optimize(CommonSubGraphBasedOptimizer.scala:77)
at org.apache.flink.table.planner.delegation.PlannerBase.optimize(PlannerBase.scala:300)
at org.apache.flink.table.planner.delegation.PlannerBase.translate(PlannerBase.scala:183)
at org.apache.flink.table.api.internal.TableEnvironmentImpl.translate(TableEnvironmentImpl.java:1704)
at org.apache.flink.table.api.internal.TableEnvironmentImpl.executeInternal(TableEnvironmentImpl.java:754)
at org.apache.flink.table.planner.utils.TestingStatementSet.execute(TableTestBase.scala:1511)
at org.apache.flink.table.planner.runtime.batch.sql.TableSourceITCase.testTableXiaojin(TableSourceITCase.scala:345)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:59)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:56)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26)
at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27)
at org.apache.flink.util.TestNameProvider$1.evaluate(TestNameProvider.java:45)
at org.junit.rules.TestWatcher$1.evaluate(TestWatcher.java:61)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.BlockJUnit4ClassRunner$1.evaluate(BlockJUnit4ClassRunner.java:100)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:366)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:103)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:63)
at org.junit.runners.ParentRunner$4.run(ParentRunner.java:331)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:79)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:329)
at org.junit.runners.ParentRunner.access$100(ParentRunner.java:66)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:293)
at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:54)
at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:54)
at org.junit.rules.RunRules.evaluate(RunRules.java:20)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.ParentRunner.run(ParentRunner.java:413)
at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:68)
at com.intellij.rt.junit.IdeaTestRunner$Repeater.startRunnerWithArgs(IdeaTestRunner.java:33)
at com.intellij.rt.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:230)
at com.intellij.rt.junit.JUnitStarter.main(JUnitStarter.java:58)
Caused by: org.apache.calcite.plan.RelOptPlanner$CannotPlanException: There are not enough rules to produce a node with desired properties: convention=BATCH_PHYSICAL, FlinkRelDistributionTraitDef=any, sort=[]. All the inputs have relevant nodes, however the cost is still infinite.
Root: rel#2372:RelSubset#84.BATCH_PHYSICAL.any.[]
Original rel:
FlinkLogicalSink(subset=[rel#118:RelSubset#2.LOGICAL.any.[]], table=[default_catalog.default_database.database5_t0], fields=[EXPR$0, EXPR$1, EXPR$2]): rowcount = 1.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 124
FlinkLogicalCalc(subset=[rel#123:RelSubset#1.LOGICAL.any.[]], select=[0.84355265:FLOAT AS EXPR$0, 0.3269016:FLOAT AS EXPR$1, _UTF-16LE' ' AS EXPR$2]): rowcount = 1.0, cumulative cost = {1.0 rows, 0.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 125
FlinkLogicalValues(subset=[rel#121:RelSubset#0.LOGICAL.any.[0]], tuples=[[{ 0 }]]): rowcount = 1.0, cumulative cost = {1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 120
Sets:
Set#57, type: RecordType(FLOAT c0)
rel#2310:RelSubset#57.LOGICAL.any.[], best=rel#2116
rel#2116:FlinkLogicalTableSourceScan.LOGICAL.any.[](table=[default_catalog, default_database, database5_t0, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 1.0E8 cpu, 4.0E8 io, 0.0 network, 0.0 memory}
rel#2375:RelSubset#57.BATCH_PHYSICAL.any.[], best=rel#2374
rel#2374:BatchPhysicalTableSourceScan.BATCH_PHYSICAL.any.[](table=[default_catalog, default_database, database5_t0, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 0.0 cpu, 4.0E8 io, 0.0 network, 0.0 memory}
rel#2379:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2375,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2508:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2375,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 4.0E8 io, 4.0E8 network, 0.0 memory}
rel#2378:RelSubset#57.BATCH_PHYSICAL.broadcast.[], best=rel#2508
rel#2379:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2375,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2508:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2375,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 4.0E8 io, 4.0E8 network, 0.0 memory}
Set#58, type: RecordType(VARCHAR(2147483647) c0)
rel#2311:RelSubset#58.LOGICAL.any.[], best=rel#2118
rel#2118:FlinkLogicalTableSourceScan.LOGICAL.any.[](table=[default_catalog, default_database, database5_t3, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 1.0E8 cpu, 1.2E9 io, 0.0 network, 0.0 memory}
rel#2377:RelSubset#58.BATCH_PHYSICAL.any.[], best=rel#2376
rel#2376:BatchPhysicalTableSourceScan.BATCH_PHYSICAL.any.[](table=[default_catalog, default_database, database5_t3, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 0.0 cpu, 1.2E9 io, 0.0 network, 0.0 memory}
Set#59, type: RecordType(FLOAT c0, VARCHAR(2147483647) c00)
rel#2313:RelSubset#59.LOGICAL.any.[], best=rel#2312
rel#2312:FlinkLogicalJoin.LOGICAL.any.[](left=RelSubset#2310,right=RelSubset#2311,condition=true,joinType=inner), rowcount=1.0E16, cumulative cost={3.0E8 rows, 4.0E8 cpu, 2.1E9 io, 0.0 network, 0.0 memory}
rel#2381:RelSubset#59.BATCH_PHYSICAL.any.[], best=rel#2380
rel#2380:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2378,right=RelSubset#2377,joinType=InnerJoin,where=true,select=c0, c00,build=left), rowcount=1.0E16, cumulative cost={1.00000003E16 rows, 1.0000016E16 cpu, 1.6E9 io, 4.0E8 network, 8.0E8 memory}
Set#60, type: RecordType(FLOAT c0)
rel#2315:RelSubset#60.LOGICAL.any.[], best=rel#2314
rel#2314:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2313,select=c0), rowcount=1.0E16, cumulative cost={1.00000003E16 rows, 4.0E8 cpu, 2.1E9 io, 0.0 network, 0.0 memory}
rel#2384:RelSubset#60.BATCH_PHYSICAL.any.[], best=rel#2383
rel#2383:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2381,select=c0), rowcount=1.0E16, cumulative cost={2.00000003E16 rows, 1.0000016E16 cpu, 1.6E9 io, 4.0E8 network, 8.0E8 memory}
Set#61, type: RecordType(TINYINT c0)
rel#2317:RelSubset#61.LOGICAL.any.[], best=rel#2123
rel#2123:FlinkLogicalTableSourceScan.LOGICAL.any.[](table=[default_catalog, default_database, database5_t1, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 1.0E8 cpu, 1.0E8 io, 0.0 network, 0.0 memory}
rel#2386:RelSubset#61.BATCH_PHYSICAL.any.[], best=rel#2385
rel#2385:BatchPhysicalTableSourceScan.BATCH_PHYSICAL.any.[](table=[default_catalog, default_database, database5_t1, project=[c0]],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 0.0 cpu, 1.0E8 io, 0.0 network, 0.0 memory}
rel#2388:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2386,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2512:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2386,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 1.0E8 io, 1.0E8 network, 0.0 memory}
rel#2387:RelSubset#61.BATCH_PHYSICAL.broadcast.[], best=rel#2512
rel#2388:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2386,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2512:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2386,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 1.0E8 io, 1.0E8 network, 0.0 memory}
Set#62, type: RecordType(FLOAT c0, TINYINT c00)
rel#2319:RelSubset#62.LOGICAL.any.[], best=rel#2318
rel#2318:FlinkLogicalJoin.LOGICAL.any.[](left=RelSubset#2315,right=RelSubset#2317,condition=true,joinType=inner), rowcount=1.0E24, cumulative cost={2.00000004E16 rows, 1.00000006E16 cpu, 4.00000023E16 io, 0.0 network, 0.0 memory}
rel#2390:RelSubset#62.BATCH_PHYSICAL.any.[], best=rel#2389
rel#2389:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2384,right=RelSubset#2387,joinType=InnerJoin,where=true,select=c0, c00,build=right), rowcount=1.0E24, cumulative cost={1.0000000200000004E24 rows, 1.0000000100000319E24 cpu, 1.7E9 io, 5.0E8 network, 1.86264518E16 memory}
Set#63, type: RecordType(FLOAT c0)
rel#2321:RelSubset#63.LOGICAL.any.[], best=rel#2320
rel#2320:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2319,select=c0), rowcount=1.0E24, cumulative cost={1.0000000200000004E24 rows, 1.00000006E16 cpu, 4.00000023E16 io, 0.0 network, 0.0 memory}
rel#2393:RelSubset#63.BATCH_PHYSICAL.any.[], best=rel#2392
rel#2392:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2390,select=c0), rowcount=1.0E24, cumulative cost={2.0000000200000005E24 rows, 1.0000000100000319E24 cpu, 1.7E9 io, 5.0E8 network, 1.86264518E16 memory}
Set#64, type: RecordType(FLOAT c0)
rel#2323:RelSubset#64.LOGICAL.any.[], best=rel#2055
rel#2055:FlinkLogicalTableSourceScan.LOGICAL.any.[](table=[default_catalog, default_database, database5_t2],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 1.0E8 cpu, 4.0E8 io, 0.0 network, 0.0 memory}
rel#2395:RelSubset#64.BATCH_PHYSICAL.any.[], best=rel#2394
rel#2394:BatchPhysicalTableSourceScan.BATCH_PHYSICAL.any.[](table=[default_catalog, default_database, database5_t2],fields=c0), rowcount=1.0E8, cumulative cost={1.0E8 rows, 0.0 cpu, 4.0E8 io, 0.0 network, 0.0 memory}
rel#2397:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2395,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2516:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2395,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 4.0E8 io, 4.0E8 network, 0.0 memory}
rel#2396:RelSubset#64.BATCH_PHYSICAL.broadcast.[], best=rel#2516
rel#2397:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2395,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0E8, cumulative cost={inf}
rel#2516:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2395,distribution=broadcast), rowcount=1.0E8, cumulative cost={2.0E8 rows, 1.6E10 cpu, 4.0E8 io, 4.0E8 network, 0.0 memory}
Set#65, type: RecordType(FLOAT c0, FLOAT c00)
rel#2325:RelSubset#65.LOGICAL.any.[], best=rel#2324
rel#2324:FlinkLogicalJoin.LOGICAL.any.[](left=RelSubset#2321,right=RelSubset#2323,condition=true,joinType=inner), rowcount=1.0E32, cumulative cost={2.0000000200000005E24 rows, 1.0000000100000008E24 cpu, 4.0000000400000026E24 io, 0.0 network, 0.0 memory}
rel#2399:RelSubset#65.BATCH_PHYSICAL.any.[], best=rel#2398
rel#2398:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2393,right=RelSubset#2396,joinType=InnerJoin,where=true,select=c0, c00,build=right), rowcount=1.0E32, cumulative cost={1.0000000200000003E32 rows, 1.0000000100000002E32 cpu, 2.1E9 io, 9.0E8 network, 1.7366133694E18 memory}
Set#66, type: RecordType(FLOAT c0)
rel#2327:RelSubset#66.LOGICAL.any.[], best=rel#2326
rel#2326:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2325,select=c0), rowcount=1.0E32, cumulative cost={1.0000000200000003E32 rows, 1.0000000100000008E24 cpu, 4.0000000400000026E24 io, 0.0 network, 0.0 memory}
rel#2402:RelSubset#66.BATCH_PHYSICAL.any.[], best=rel#2401
rel#2401:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2399,select=c0), rowcount=1.0E32, cumulative cost={2.0000000200000004E32 rows, 1.0000000100000002E32 cpu, 2.1E9 io, 9.0E8 network, 1.7366133694E18 memory}
Set#67, type: RecordType(INTEGER ZERO)
rel#2329:RelSubset#67.LOGICAL.any.[0], best=rel#2063
rel#2063:FlinkLogicalValues.LOGICAL.any.[0](type=RecordType(INTEGER ZERO),tuples=[{ 0 }]), rowcount=1.0, cumulative cost={1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2404:RelSubset#67.BATCH_PHYSICAL.any.[0], best=rel#2403
rel#2403:BatchPhysicalValues.BATCH_PHYSICAL.any.[0](type=RecordType(INTEGER ZERO),tuples=[{ 0 }],values=ZERO), rowcount=1.0, cumulative cost={1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2523:AbstractConverter.BATCH_PHYSICAL.single.[0](input=RelSubset#2404,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[0]), rowcount=1.0, cumulative cost={inf}
rel#2561:BatchPhysicalSort.BATCH_PHYSICAL.single.[0](input=RelSubset#2560,orderBy=ZERO ASC), rowcount=1.0, cumulative cost={3.0 rows, 166.0 cpu, 0.0 io, 4.0 network, 44.0 memory}
rel#2522:RelSubset#67.BATCH_PHYSICAL.single.[0], best=rel#2561
rel#2523:AbstractConverter.BATCH_PHYSICAL.single.[0](input=RelSubset#2404,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[0]), rowcount=1.0, cumulative cost={inf}
rel#2561:BatchPhysicalSort.BATCH_PHYSICAL.single.[0](input=RelSubset#2560,orderBy=ZERO ASC), rowcount=1.0, cumulative cost={3.0 rows, 166.0 cpu, 0.0 io, 4.0 network, 44.0 memory}
Set#68, type: RecordType(BOOLEAN i)
rel#2331:RelSubset#68.LOGICAL.any.[], best=rel#2330
rel#2330:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2329,select=true AS i), rowcount=1.0, cumulative cost={2.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2406:RelSubset#68.BATCH_PHYSICAL.any.[], best=rel#2405
rel#2405:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2404,select=true AS i), rowcount=1.0, cumulative cost={2.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2414:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2406,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2521:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2406,distribution=single), rowcount=1.0, cumulative cost={3.0 rows, 162.0 cpu, 0.0 io, 1.0 network, 0.0 memory}
rel#2524:BatchPhysicalCalc.BATCH_PHYSICAL.single.[](input=RelSubset#2522,select=true AS i), rowcount=1.0, cumulative cost={4.0 rows, 166.0 cpu, 0.0 io, 4.0 network, 44.0 memory}
rel#2413:RelSubset#68.BATCH_PHYSICAL.single.[], best=rel#2521
rel#2414:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2406,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2521:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2406,distribution=single), rowcount=1.0, cumulative cost={3.0 rows, 162.0 cpu, 0.0 io, 1.0 network, 0.0 memory}
rel#2524:BatchPhysicalCalc.BATCH_PHYSICAL.single.[](input=RelSubset#2522,select=true AS i), rowcount=1.0, cumulative cost={4.0 rows, 166.0 cpu, 0.0 io, 4.0 network, 44.0 memory}
Set#69, type: RecordType(BOOLEAN m)
rel#2333:RelSubset#69.LOGICAL.any.[], best=rel#2332
rel#2332:FlinkLogicalAggregate.LOGICAL.any.[](input=RelSubset#2331,group={},m=MIN($0)), rowcount=1.0, cumulative cost={3.0 rows, 2.0 cpu, 1.0 io, 0.0 network, 0.0 memory}
rel#2412:RelSubset#69.BATCH_PHYSICAL.any.[], best=rel#2415
rel#2411:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2409,isMerge=true,select=Final_MIN(min$0) AS m), rowcount=1.0, cumulative cost={5.0 rows, 186.0 cpu, 0.0 io, 1.0 network, 2.0 memory}
rel#2415:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2413,isMerge=false,select=MIN(i) AS m), rowcount=1.0, cumulative cost={4.0 rows, 174.0 cpu, 0.0 io, 1.0 network, 1.0 memory}
rel#2420:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2418,isMerge=true,select=Final_MIN(min$0) AS m), rowcount=1.0, cumulative cost={5.0 rows, 186.0 cpu, 0.0 io, 1.0 network, 2.0 memory}
rel#2421:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2413,isMerge=false,select=MIN(i) AS m), rowcount=1.0, cumulative cost={4.0 rows, 174.0 cpu, 0.0 io, 1.0 network, 1.0 memory}
Set#70, type: RecordType(BOOLEAN $f0)
rel#2335:RelSubset#70.LOGICAL.any.[], best=rel#2334
rel#2334:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2333,select=IS NOT NULL(m) AS $f0), rowcount=1.0, cumulative cost={4.0 rows, 3.0 cpu, 1.0 io, 0.0 network, 0.0 memory}
rel#2423:RelSubset#70.BATCH_PHYSICAL.any.[], best=rel#2422
rel#2422:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2412,select=IS NOT NULL(m) AS $f0), rowcount=1.0, cumulative cost={5.0 rows, 175.0 cpu, 0.0 io, 1.0 network, 1.0 memory}
rel#2425:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2423,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2528:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2423,distribution=broadcast), rowcount=1.0, cumulative cost={6.0 rows, 335.0 cpu, 0.0 io, 2.0 network, 1.0 memory}
rel#2424:RelSubset#70.BATCH_PHYSICAL.broadcast.[], best=rel#2528
rel#2425:AbstractConverter.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2423,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=broadcast,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2528:BatchPhysicalExchange.BATCH_PHYSICAL.broadcast.[](input=RelSubset#2423,distribution=broadcast), rowcount=1.0, cumulative cost={6.0 rows, 335.0 cpu, 0.0 io, 2.0 network, 1.0 memory}
Set#71, type: RecordType(FLOAT c0)
rel#2337:RelSubset#71.LOGICAL.any.[], best=rel#2336
rel#2336:FlinkLogicalJoin.LOGICAL.any.[](left=RelSubset#2327,right=RelSubset#2335,condition=$1,joinType=semi), rowcount=2.5E31, cumulative cost={2.0000000200000004E32 rows, 1.0000000100000002E32 cpu, 4.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2427:RelSubset#71.BATCH_PHYSICAL.any.[], best=rel#2428
rel#2426:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2402,right=RelSubset#2424,joinType=LeftSemiJoin,where=$f0,select=c0,build=right), rowcount=2.5E31, cumulative cost={2.2500000200000004E32 rows, 2.0000000100000002E32 cpu, 2.1E9 io, 9.00000002E8 network, 1.73661338013741824E18 memory}
rel#2428:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2402,right=RelSubset#2424,joinType=LeftSemiJoin,where=$f0,select=c0,build=right,singleRowJoin=true), rowcount=2.5E31, cumulative cost={2.2475000200000005E32 rows, 1.9900000100000002E32 cpu, 2.1E9 io, 9.00000002E8 network, 1.73661338003004416E18 memory}
rel#2436:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2427,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=2.5E31, cumulative cost={inf}
rel#2532:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2427,distribution=single), rowcount=2.5E31, cumulative cost={2.4975000200000006E32 rows, 4.224000001E33 cpu, 2.1E9 io, 1.0E32 network, 1.73661338003004416E18 memory}
rel#2435:RelSubset#71.BATCH_PHYSICAL.single.[], best=rel#2532
rel#2436:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2427,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=2.5E31, cumulative cost={inf}
rel#2532:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2427,distribution=single), rowcount=2.5E31, cumulative cost={2.4975000200000006E32 rows, 4.224000001E33 cpu, 2.1E9 io, 1.0E32 network, 1.73661338003004416E18 memory}
Set#72, type: RecordType(BIGINT ref0)
rel#2339:RelSubset#72.LOGICAL.any.[], best=rel#2338
rel#2338:FlinkLogicalAggregate.LOGICAL.any.[](input=RelSubset#2337,group={},ref0=COUNT()), rowcount=1.0, cumulative cost={2.2500000200000004E32 rows, 1.2500000100000003E32 cpu, 5.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2434:RelSubset#72.BATCH_PHYSICAL.any.[], best=rel#2433
rel#2433:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2431,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={2.2475000200000005E32 rows, 4.99000001E32 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
rel#2437:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2435,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={2.4975000200000006E32 rows, 4.524000001E33 cpu, 2.1E9 io, 1.0E32 network, 1.73661338003004416E18 memory}
rel#2442:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2440,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={2.2475000200000005E32 rows, 4.99000001E32 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
rel#2443:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2435,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={2.4975000200000006E32 rows, 4.524000001E33 cpu, 2.1E9 io, 1.0E32 network, 1.73661338003004416E18 memory}
Set#73, type: RecordType(INTEGER $f0)
rel#2341:RelSubset#73.LOGICAL.any.[], best=rel#2340
rel#2340:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2339,select=0 AS $f0), rowcount=1.0, cumulative cost={2.2500000200000004E32 rows, 1.2500000100000003E32 cpu, 5.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2445:RelSubset#73.BATCH_PHYSICAL.any.[], best=rel#2444
rel#2444:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2434,select=0 AS $f0), rowcount=1.0, cumulative cost={2.2475000200000005E32 rows, 4.99000001E32 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
Set#74, type: RecordType(FLOAT c0)
rel#2352:RelSubset#74.LOGICAL.any.[], best=rel#2351
rel#2351:FlinkLogicalJoin.LOGICAL.any.[](left=RelSubset#2327,right=RelSubset#2335,condition=$1,joinType=anti), rowcount=7.5E31, cumulative cost={2.0000000200000004E32 rows, 1.0000000100000002E32 cpu, 4.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2447:RelSubset#74.BATCH_PHYSICAL.any.[], best=rel#2448
rel#2446:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2402,right=RelSubset#2424,joinType=LeftAntiJoin,where=$f0,select=c0,build=right), rowcount=7.5E31, cumulative cost={2.7500000200000006E32 rows, 2.0000000100000002E32 cpu, 2.1E9 io, 9.00000002E8 network, 1.73661338013741824E18 memory}
rel#2448:BatchPhysicalNestedLoopJoin.BATCH_PHYSICAL.any.[](left=RelSubset#2402,right=RelSubset#2424,joinType=LeftAntiJoin,where=$f0,select=c0,build=right,singleRowJoin=true), rowcount=7.5E31, cumulative cost={2.7425000200000004E32 rows, 1.9900000100000002E32 cpu, 2.1E9 io, 9.00000002E8 network, 1.73661338003004416E18 memory}
rel#2456:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2447,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=7.5E31, cumulative cost={inf}
rel#2540:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2447,distribution=single), rowcount=7.5E31, cumulative cost={3.49250002E32 rows, 1.2274000001E34 cpu, 2.1E9 io, 3.0E32 network, 1.73661338003004416E18 memory}
rel#2455:RelSubset#74.BATCH_PHYSICAL.single.[], best=rel#2540
rel#2456:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2447,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=7.5E31, cumulative cost={inf}
rel#2540:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2447,distribution=single), rowcount=7.5E31, cumulative cost={3.49250002E32 rows, 1.2274000001E34 cpu, 2.1E9 io, 3.0E32 network, 1.73661338003004416E18 memory}
Set#75, type: RecordType(BIGINT ref0)
rel#2354:RelSubset#75.LOGICAL.any.[], best=rel#2353
rel#2353:FlinkLogicalAggregate.LOGICAL.any.[](input=RelSubset#2352,group={},ref0=COUNT()), rowcount=1.0, cumulative cost={2.7500000200000006E32 rows, 1.7500000100000002E32 cpu, 7.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2454:RelSubset#75.BATCH_PHYSICAL.any.[], best=rel#2453
rel#2453:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2451,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={2.7425000200000004E32 rows, 1.099000001E33 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
rel#2457:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2455,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={3.49250002E32 rows, 1.3174000001E34 cpu, 2.1E9 io, 3.0E32 network, 1.73661338003004416E18 memory}
rel#2462:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2460,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={2.7425000200000004E32 rows, 1.099000001E33 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
rel#2463:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2455,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={3.49250002E32 rows, 1.3174000001E34 cpu, 2.1E9 io, 3.0E32 network, 1.73661338003004416E18 memory}
Set#76, type: RecordType(INTEGER $f0)
rel#2356:RelSubset#76.LOGICAL.any.[], best=rel#2355
rel#2355:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2354,select=0 AS $f0), rowcount=1.0, cumulative cost={2.7500000200000006E32 rows, 1.7500000100000002E32 cpu, 7.000000040000001E32 io, 0.0 network, 0.0 memory}
rel#2465:RelSubset#76.BATCH_PHYSICAL.any.[], best=rel#2464
rel#2464:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2454,select=0 AS $f0), rowcount=1.0, cumulative cost={2.7425000200000004E32 rows, 1.099000001E33 cpu, 2.1E9 io, 1.8079869178E10 network, 1.73661338003004416E18 memory}
Set#77, type: RecordType(INTEGER $f0)
rel#2358:RelSubset#77.LOGICAL.any.[], best=rel#2357
rel#2357:FlinkLogicalUnion.LOGICAL.any.[](input#0=RelSubset#2341,input#1=RelSubset#2356,all=true), rowcount=2.0, cumulative cost={5.000000040000001E32 rows, 3.0000000200000003E32 cpu, 1.2000000080000001E33 io, 0.0 network, 0.0 memory}
rel#2467:RelSubset#77.BATCH_PHYSICAL.any.[], best=rel#2466
rel#2466:BatchPhysicalUnion.BATCH_PHYSICAL.any.[](input#0=RelSubset#2445,input#1=RelSubset#2465,all=true,union=$f0), rowcount=2.0, cumulative cost={4.9900000400000006E32 rows, 1.5980000020000002E33 cpu, 4.2E9 io, 3.6159738356E10 network, 3.4732267600600883E18 memory}
Set#78, type: RecordType(INTEGER $f0)
rel#2359:RelSubset#78.LOGICAL.any.[], best=rel#2103
rel#2103:FlinkLogicalValues.LOGICAL.any.[](type=RecordType(INTEGER $f0),tuples=[]), rowcount=1.0, cumulative cost={1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2469:RelSubset#78.BATCH_PHYSICAL.any.[], best=rel#2468
rel#2468:BatchPhysicalValues.BATCH_PHYSICAL.any.[](type=RecordType(INTEGER $f0),tuples=[],values=$f0), rowcount=1.0, cumulative cost={1.0 rows, 1.0 cpu, 0.0 io, 0.0 network, 0.0 memory}
rel#2477:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2469,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2548:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2469,distribution=single), rowcount=1.0, cumulative cost={2.0 rows, 162.0 cpu, 0.0 io, 4.0 network, 0.0 memory}
rel#2476:RelSubset#78.BATCH_PHYSICAL.single.[], best=rel#2548
rel#2477:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2469,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[]), rowcount=1.0, cumulative cost={inf}
rel#2548:BatchPhysicalExchange.BATCH_PHYSICAL.single.[](input=RelSubset#2469,distribution=single), rowcount=1.0, cumulative cost={2.0 rows, 162.0 cpu, 0.0 io, 4.0 network, 0.0 memory}
Set#79, type: RecordType(BIGINT ref0)
rel#2361:RelSubset#79.LOGICAL.any.[], best=rel#2360
rel#2360:FlinkLogicalAggregate.LOGICAL.any.[](input=RelSubset#2359,group={},ref0=COUNT()), rowcount=1.0, cumulative cost={2.0 rows, 2.0 cpu, 4.0 io, 0.0 network, 0.0 memory}
rel#2475:RelSubset#79.BATCH_PHYSICAL.any.[], best=rel#2478
rel#2474:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2472,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={4.0 rows, 186.0 cpu, 0.0 io, 8.0 network, 16.0 memory}
rel#2478:BatchPhysicalHashAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2476,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={3.0 rows, 174.0 cpu, 0.0 io, 4.0 network, 8.0 memory}
rel#2483:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2481,isMerge=true,select=Final_COUNT(count1$0) AS ref0), rowcount=1.0, cumulative cost={4.0 rows, 186.0 cpu, 0.0 io, 8.0 network, 16.0 memory}
rel#2484:BatchPhysicalSortAggregate.BATCH_PHYSICAL.any.[](input=RelSubset#2476,isMerge=false,select=COUNT(*) AS ref0), rowcount=1.0, cumulative cost={3.0 rows, 174.0 cpu, 0.0 io, 4.0 network, 8.0 memory}
Set#80, type: RecordType(INTEGER $f0)
rel#2363:RelSubset#80.LOGICAL.any.[], best=rel#2362
rel#2362:FlinkLogicalCalc.LOGICAL.any.[](input=RelSubset#2361,select=0 AS $f0), rowcount=1.0, cumulative cost={3.0 rows, 2.0 cpu, 4.0 io, 0.0 network, 0.0 memory}
rel#2486:RelSubset#80.BATCH_PHYSICAL.any.[], best=rel#2485
rel#2485:BatchPhysicalCalc.BATCH_PHYSICAL.any.[](input=RelSubset#2475,select=0 AS $f0), rowcount=1.0, cumulative cost={4.0 rows, 174.0 cpu, 0.0 io, 4.0 network, 8.0 memory}
Set#81, type: RecordType(INTEGER $f0)
rel#2365:RelSubset#81.LOGICAL.any.[], best=rel#2364
rel#2364:FlinkLogicalUnion.LOGICAL.any.[](input#0=RelSubset#2358,input#1=RelSubset#2363,all=true), rowcount=3.0, cumulative cost={5.000000040000001E32 rows, 3.0000000200000003E32 cpu, 1.2000000080000001E33 io, 0.0 network, 0.0 memory}
rel#2488:RelSubset#81.BATCH_PHYSICAL.any.[], best=rel#2554
rel#2487:BatchPhysicalUnion.BATCH_PHYSICAL.any.[](input#0=RelSubset#2467,input#1=RelSubset#2486,all=true,union=$f0), rowcount=3.0, cumulative cost={4.9900000400000006E32 rows, 1.5980000020000002E33 cpu, 4.2E9 io, 3.615973836E10 network, 3.4732267600600883E18 memory}
rel#2496:AbstractConverter.BATCH_PHYSICAL.single.[](input=RelSubset#2488,convention=BATCH_PHYSICAL,FlinkRelDistributionTraitDef=single,sort=[])Error when dumping plan state:
org.apache.calcite.rel.metadata.CyclicMetadataException
at org.apache.calcite.plan.volcano.RelSubset$CheapestPlanReplacer.visit(RelSubset.java:742)
at org.apache.calcite.plan.volcano.RelSubset.buildCheapestPlan(RelSubset.java:365)
at org.apache.calcite.plan.volcano.VolcanoPlanner.findBestExp(VolcanoPlanner.java:520)
at org.apache.calcite.tools.Programs$RuleSetProgram.run(Programs.java:312)
at org.apache.flink.table.planner.plan.optimize.program.FlinkVolcanoProgram.optimize(FlinkVolcanoProgram.scala:64)
... 56 more
{code}
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