[
https://issues.apache.org/jira/browse/IGNITE-22936?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Konstantin Orlov reassigned IGNITE-22936:
-----------------------------------------
Assignee: Konstantin Orlov
> Sql. Confusing error message when trying to use recursive CTE
> -------------------------------------------------------------
>
> Key: IGNITE-22936
> URL: https://issues.apache.org/jira/browse/IGNITE-22936
> Project: Ignite
> Issue Type: Improvement
> Components: sql
> Reporter: Pavel Pereslegin
> Assignee: Konstantin Orlov
> Priority: Major
> Labels: ignite-3
>
> We don't currently support recursive common table expressions (IGNITE-22866).
> However, the error output for such queries is huge and confusing because it
> contains debugging information.
> For example:
> {code:sql}
> WITH RECURSIVE t(n) AS (SELECT 1 UNION ALL SELECT n + 1 FROM t WHERE n < 100)
> SELECT sum(n) FROM t;
> {code}
> Produces the following error
> {noformat}
> org.apache.ignite.sql.SqlException: IGN-CMN-65535
> TraceId:c2961dbf-64fd-4806-a2d9-20368f8454e5 There are not enough rules to
> produce a node with desired properties: convention=IGNITE, distr=single,
> sort=[].
> Missing conversions are LogicalRepeatUnion[convention: NONE -> IGNITE, distr:
> any -> single], LogicalRepeatUnion[convention: NONE -> IGNITE, distr: any ->
> random]
> There are 2 empty subsets:
> Empty subset 0: rel#92:RelSubset#6.IGNITE.single.[], the relevant part of the
> original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Empty subset 1: rel#102:RelSubset#6.IGNITE.random.[], the relevant part of
> the original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Root: rel#91:RelSubset#7.IGNITE.single.[]
> Original rel:
> LogicalAggregate(subset=[rel#91:RelSubset#7.IGNITE.single.[]], group=[{}],
> EXPR$0=[SUM($0)]): rowcount = 1.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 89
> LogicalRepeatUnion(subset=[rel#88:RelSubset#6.NONE.any.[]], all=[true]):
> rowcount = 501.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 87
> LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 1.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 78
> LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]]): rowcount = 1.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 1
> LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 50.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 85
> LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)]): rowcount = 50.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 83
> LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)]): rowcount = 50.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 81
> LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]]): rowcount = 100.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 2
> Sets:
> Set#0, type: RecordType(INTEGER EXPR$0)
> rel#77:RelSubset#0.NONE.any.[0], best=null
> rel#1:LogicalValues.NONE.any.[0](type=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]), rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity]
> Set#1, type: RecordType(INTEGER EXPR$0)
> rel#79:RelSubset#1.NONE.any.[0], best=null
> rel#78:LogicalTableSpool.NONE.any.[0](input=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#2, type: RecordType(INTEGER N)
> rel#80:RelSubset#2.NONE.any.[], best=null
> rel#2:LogicalTableScan.NONE.any.[](table=[T]), rowcount=100.0, cumulative
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]
> Set#3, type: RecordType(INTEGER N)
> rel#82:RelSubset#3.NONE.any.[], best=null
> rel#81:LogicalFilter.NONE.any.[](input=RelSubset#80,condition=<($0, 100)),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#4, type: RecordType(INTEGER EXPR$0)
> rel#84:RelSubset#4.NONE.any.[], best=null
> rel#83:LogicalProject.NONE.any.[](input=RelSubset#82,exprs=[+($0, 1)]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#5, type: RecordType(INTEGER EXPR$0)
> rel#86:RelSubset#5.NONE.any.[], best=null
> rel#85:LogicalTableSpool.NONE.any.[](input=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#6, type: RecordType(INTEGER EXPR$0)
> rel#88:RelSubset#6.NONE.any.[], best=null
> rel#87:LogicalRepeatUnion.NONE.any.[](left=RelSubset#79,right=RelSubset#86,all=true),
> rowcount=501.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#92:RelSubset#6.IGNITE.single.[], best=null
> rel#102:RelSubset#6.IGNITE.random.[], best=null
> Set#7, type: RecordType(BIGINT EXPR$0)
> rel#90:RelSubset#7.NONE.any.[], best=null
> rel#89:LogicalAggregate.NONE.any.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#91:RelSubset#7.IGNITE.single.[], best=null
> rel#93:ColocatedHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#98:ReduceHashAggregate.IGNITE.single.[](input=RelSubset#96,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#101:ColocatedSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#107:ReduceSortAggregate.IGNITE.single.[](input=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#8, type: RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)
> rel#95:RelSubset#8.IGNITE.random.[], best=null
> rel#94:MapHashAggregate.IGNITE.random.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#96:RelSubset#8.IGNITE.single.[], best=null
> rel#97:Exchange.IGNITE.single.[](input=RelSubset#95,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#99:MapHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#9, type: RecordType(DECIMAL(32767, 0) _ACC0)
> rel#104:RelSubset#9.IGNITE.random.[], best=null
> rel#103:MapSortAggregate.IGNITE.random.[](input=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#105:RelSubset#9.IGNITE.single.[], best=null
> rel#106:Exchange.IGNITE.single.[](input=RelSubset#104,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#108:MapSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Graphviz:
> digraph G {
> root [style=filled,label="Root"];
> subgraph cluster0{
> label="Set 0 RecordType(INTEGER EXPR$0)";
> rel1 [label="rel#1:LogicalValues\ntype=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]\nrows=1.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset77 [label="rel#77:RelSubset#0.NONE.any.[0]"]
> }
> subgraph cluster1{
> label="Set 1 RecordType(INTEGER EXPR$0)";
> rel78
> [label="rel#78:LogicalTableSpool\ninput=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset79 [label="rel#79:RelSubset#1.NONE.any.[0]"]
> }
> subgraph cluster2{
> label="Set 2 RecordType(INTEGER N)";
> rel2 [label="rel#2:LogicalTableScan\ntable=[T]\nrows=100.0, cost=IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity]",shape=box]
> subset80 [label="rel#80:RelSubset#2.NONE.any.[]"]
> }
> subgraph cluster3{
> label="Set 3 RecordType(INTEGER N)";
> rel81 [label="rel#81:LogicalFilter\ninput=RelSubset#80,condition=<($0,
> 100)\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset82 [label="rel#82:RelSubset#3.NONE.any.[]"]
> }
> subgraph cluster4{
> label="Set 4 RecordType(INTEGER EXPR$0)";
> rel83 [label="rel#83:LogicalProject\ninput=RelSubset#82,exprs=[+($0,
> 1)]\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset84 [label="rel#84:RelSubset#4.NONE.any.[]"]
> }
> subgraph cluster5{
> label="Set 5 RecordType(INTEGER EXPR$0)";
> rel85
> [label="rel#85:LogicalTableSpool\ninput=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]\nrows=50.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset86 [label="rel#86:RelSubset#5.NONE.any.[]"]
> }
> subgraph cluster6{
> label="Set 6 RecordType(INTEGER EXPR$0)";
> rel87
> [label="rel#87:LogicalRepeatUnion\nleft=RelSubset#79,right=RelSubset#86,all=true\nrows=501.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset88 [label="rel#88:RelSubset#6.NONE.any.[]"]
> subset92 [label="rel#92:RelSubset#6.IGNITE.single.[]",color=red]
> subset102 [label="rel#102:RelSubset#6.IGNITE.random.[]",color=red]
> }
> subgraph cluster7{
> label="Set 7 RecordType(BIGINT EXPR$0)";
> rel89
> [label="rel#89:LogicalAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel93
> [label="rel#93:ColocatedHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel98
> [label="rel#98:ReduceHashAggregate\ninput=RelSubset#96,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel101
> [label="rel#101:ColocatedSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel107
> [label="rel#107:ReduceSortAggregate\ninput=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset90 [label="rel#90:RelSubset#7.NONE.any.[]"]
> subset91 [label="rel#91:RelSubset#7.IGNITE.single.[]"]
> }
> subgraph cluster8{
> label="Set 8 RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)";
> rel94
> [label="rel#94:MapHashAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel97
> [label="rel#97:Exchange\ninput=RelSubset#95,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel99
> [label="rel#99:MapHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset95 [label="rel#95:RelSubset#8.IGNITE.random.[]"]
> subset96 [label="rel#96:RelSubset#8.IGNITE.single.[]"]
> }
> subgraph cluster9{
> label="Set 9 RecordType(DECIMAL(32767, 0) _ACC0)";
> rel103
> [label="rel#103:MapSortAggregate\ninput=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel106
> [label="rel#106:Exchange\ninput=RelSubset#104,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel108
> [label="rel#108:MapSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset104 [label="rel#104:RelSubset#9.IGNITE.random.[]"]
> subset105 [label="rel#105:RelSubset#9.IGNITE.single.[]"]
> }
> root -> subset91;
> subset77 -> rel1;
> subset79 -> rel78; rel78 -> subset77;
> subset80 -> rel2;
> subset82 -> rel81; rel81 -> subset80;
> subset84 -> rel83; rel83 -> subset82;
> subset86 -> rel85; rel85 -> subset84;
> subset88 -> rel87; rel87 -> subset79[label="0"]; rel87 -> subset86[label="1"];
> subset90 -> rel89; rel89 -> subset88;
> subset91 -> rel93; rel93 -> subset92;
> subset91 -> rel98; rel98 -> subset96;
> subset91 -> rel101; rel101 -> subset92;
> subset91 -> rel107; rel107 -> subset105;
> subset95 -> rel94; rel94 -> subset88;
> subset96 -> rel97; rel97 -> subset95;
> subset96 -> rel99; rel99 -> subset92;
> subset104 -> rel103; rel103 -> subset102;
> subset105 -> rel106; rel106 -> subset104;
> subset105 -> rel108; rel108 -> subset92;
> }
> at
> java.base/java.lang.invoke.MethodHandle.invokeWithArguments(MethodHandle.java:710)
> at
> org.apache.ignite.internal.util.ExceptionUtils$1.copy(ExceptionUtils.java:789)
> at
> org.apache.ignite.internal.util.ExceptionUtils$ExceptionFactory.createCopy(ExceptionUtils.java:723)
> at
> org.apache.ignite.internal.util.ExceptionUtils.copyExceptionWithCause(ExceptionUtils.java:525)
> at
> org.apache.ignite.internal.util.ExceptionUtils.copyExceptionWithCauseInternal(ExceptionUtils.java:658)
> at
> org.apache.ignite.internal.util.ExceptionUtils.copyExceptionWithCause(ExceptionUtils.java:476)
> at
> org.apache.ignite.internal.sql.api.IgniteSqlImpl.execute(IgniteSqlImpl.java:222)
> at
> org.apache.ignite.internal.sql.api.PublicApiThreadingIgniteSql.lambda$execute$2(PublicApiThreadingIgniteSql.java:70)
> at
> org.apache.ignite.internal.thread.PublicApiThreading.executeWithRole(PublicApiThreading.java:144)
> at
> org.apache.ignite.internal.thread.PublicApiThreading.execUserSyncOperation(PublicApiThreading.java:102)
> at
> org.apache.ignite.internal.sql.api.PublicApiThreadingIgniteSql.execute(PublicApiThreadingIgniteSql.java:70)
> at
> org.apache.ignite.internal.ClusterPerClassIntegrationTest.sql(ClusterPerClassIntegrationTest.java:376)
> at
> org.apache.ignite.internal.ClusterPerClassIntegrationTest.sql(ClusterPerClassIntegrationTest.java:390)
> at
> org.apache.ignite.internal.ClusterPerClassIntegrationTest.sql(ClusterPerClassIntegrationTest.java:386)
> at
> org.apache.ignite.internal.ClusterPerClassIntegrationTest.sql(ClusterPerClassIntegrationTest.java:382)
> at
> org.apache.ignite.internal.ClusterPerClassIntegrationTest.sql(ClusterPerClassIntegrationTest.java:349)
> at
> org.apache.ignite.internal.sql.api.ItSqlApiBaseTest.testX(ItSqlApiBaseTest.java:94)
> at java.base/java.lang.reflect.Method.invoke(Method.java:566)
> at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
> at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
> Caused by: java.util.concurrent.CompletionException:
> org.apache.ignite.sql.SqlException: IGN-CMN-65535
> TraceId:c2961dbf-64fd-4806-a2d9-20368f8454e5 There are not enough rules to
> produce a node with desired properties: convention=IGNITE, distr=single,
> sort=[].
> Missing conversions are LogicalRepeatUnion[convention: NONE -> IGNITE, distr:
> any -> single], LogicalRepeatUnion[convention: NONE -> IGNITE, distr: any ->
> random]
> There are 2 empty subsets:
> Empty subset 0: rel#92:RelSubset#6.IGNITE.single.[], the relevant part of the
> original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Empty subset 1: rel#102:RelSubset#6.IGNITE.random.[], the relevant part of
> the original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Root: rel#91:RelSubset#7.IGNITE.single.[]
> Original rel:
> LogicalAggregate(subset=[rel#91:RelSubset#7.IGNITE.single.[]], group=[{}],
> EXPR$0=[SUM($0)]): rowcount = 1.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 89
> LogicalRepeatUnion(subset=[rel#88:RelSubset#6.NONE.any.[]], all=[true]):
> rowcount = 501.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 87
> LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 1.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 78
> LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]]): rowcount = 1.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 1
> LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 50.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 85
> LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)]): rowcount = 50.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 83
> LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)]): rowcount = 50.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 81
> LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]]): rowcount = 100.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 2
> Sets:
> Set#0, type: RecordType(INTEGER EXPR$0)
> rel#77:RelSubset#0.NONE.any.[0], best=null
> rel#1:LogicalValues.NONE.any.[0](type=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]), rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity]
> Set#1, type: RecordType(INTEGER EXPR$0)
> rel#79:RelSubset#1.NONE.any.[0], best=null
> rel#78:LogicalTableSpool.NONE.any.[0](input=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#2, type: RecordType(INTEGER N)
> rel#80:RelSubset#2.NONE.any.[], best=null
> rel#2:LogicalTableScan.NONE.any.[](table=[T]), rowcount=100.0, cumulative
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]
> Set#3, type: RecordType(INTEGER N)
> rel#82:RelSubset#3.NONE.any.[], best=null
> rel#81:LogicalFilter.NONE.any.[](input=RelSubset#80,condition=<($0, 100)),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#4, type: RecordType(INTEGER EXPR$0)
> rel#84:RelSubset#4.NONE.any.[], best=null
> rel#83:LogicalProject.NONE.any.[](input=RelSubset#82,exprs=[+($0, 1)]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#5, type: RecordType(INTEGER EXPR$0)
> rel#86:RelSubset#5.NONE.any.[], best=null
> rel#85:LogicalTableSpool.NONE.any.[](input=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#6, type: RecordType(INTEGER EXPR$0)
> rel#88:RelSubset#6.NONE.any.[], best=null
> rel#87:LogicalRepeatUnion.NONE.any.[](left=RelSubset#79,right=RelSubset#86,all=true),
> rowcount=501.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#92:RelSubset#6.IGNITE.single.[], best=null
> rel#102:RelSubset#6.IGNITE.random.[], best=null
> Set#7, type: RecordType(BIGINT EXPR$0)
> rel#90:RelSubset#7.NONE.any.[], best=null
> rel#89:LogicalAggregate.NONE.any.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#91:RelSubset#7.IGNITE.single.[], best=null
> rel#93:ColocatedHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#98:ReduceHashAggregate.IGNITE.single.[](input=RelSubset#96,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#101:ColocatedSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#107:ReduceSortAggregate.IGNITE.single.[](input=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#8, type: RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)
> rel#95:RelSubset#8.IGNITE.random.[], best=null
> rel#94:MapHashAggregate.IGNITE.random.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#96:RelSubset#8.IGNITE.single.[], best=null
> rel#97:Exchange.IGNITE.single.[](input=RelSubset#95,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#99:MapHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#9, type: RecordType(DECIMAL(32767, 0) _ACC0)
> rel#104:RelSubset#9.IGNITE.random.[], best=null
> rel#103:MapSortAggregate.IGNITE.random.[](input=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#105:RelSubset#9.IGNITE.single.[], best=null
> rel#106:Exchange.IGNITE.single.[](input=RelSubset#104,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#108:MapSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Graphviz:
> digraph G {
> root [style=filled,label="Root"];
> subgraph cluster0{
> label="Set 0 RecordType(INTEGER EXPR$0)";
> rel1 [label="rel#1:LogicalValues\ntype=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]\nrows=1.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset77 [label="rel#77:RelSubset#0.NONE.any.[0]"]
> }
> subgraph cluster1{
> label="Set 1 RecordType(INTEGER EXPR$0)";
> rel78
> [label="rel#78:LogicalTableSpool\ninput=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset79 [label="rel#79:RelSubset#1.NONE.any.[0]"]
> }
> subgraph cluster2{
> label="Set 2 RecordType(INTEGER N)";
> rel2 [label="rel#2:LogicalTableScan\ntable=[T]\nrows=100.0, cost=IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity]",shape=box]
> subset80 [label="rel#80:RelSubset#2.NONE.any.[]"]
> }
> subgraph cluster3{
> label="Set 3 RecordType(INTEGER N)";
> rel81 [label="rel#81:LogicalFilter\ninput=RelSubset#80,condition=<($0,
> 100)\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset82 [label="rel#82:RelSubset#3.NONE.any.[]"]
> }
> subgraph cluster4{
> label="Set 4 RecordType(INTEGER EXPR$0)";
> rel83 [label="rel#83:LogicalProject\ninput=RelSubset#82,exprs=[+($0,
> 1)]\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset84 [label="rel#84:RelSubset#4.NONE.any.[]"]
> }
> subgraph cluster5{
> label="Set 5 RecordType(INTEGER EXPR$0)";
> rel85
> [label="rel#85:LogicalTableSpool\ninput=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]\nrows=50.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset86 [label="rel#86:RelSubset#5.NONE.any.[]"]
> }
> subgraph cluster6{
> label="Set 6 RecordType(INTEGER EXPR$0)";
> rel87
> [label="rel#87:LogicalRepeatUnion\nleft=RelSubset#79,right=RelSubset#86,all=true\nrows=501.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset88 [label="rel#88:RelSubset#6.NONE.any.[]"]
> subset92 [label="rel#92:RelSubset#6.IGNITE.single.[]",color=red]
> subset102 [label="rel#102:RelSubset#6.IGNITE.random.[]",color=red]
> }
> subgraph cluster7{
> label="Set 7 RecordType(BIGINT EXPR$0)";
> rel89
> [label="rel#89:LogicalAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel93
> [label="rel#93:ColocatedHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel98
> [label="rel#98:ReduceHashAggregate\ninput=RelSubset#96,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel101
> [label="rel#101:ColocatedSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel107
> [label="rel#107:ReduceSortAggregate\ninput=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset90 [label="rel#90:RelSubset#7.NONE.any.[]"]
> subset91 [label="rel#91:RelSubset#7.IGNITE.single.[]"]
> }
> subgraph cluster8{
> label="Set 8 RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)";
> rel94
> [label="rel#94:MapHashAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel97
> [label="rel#97:Exchange\ninput=RelSubset#95,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel99
> [label="rel#99:MapHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset95 [label="rel#95:RelSubset#8.IGNITE.random.[]"]
> subset96 [label="rel#96:RelSubset#8.IGNITE.single.[]"]
> }
> subgraph cluster9{
> label="Set 9 RecordType(DECIMAL(32767, 0) _ACC0)";
> rel103
> [label="rel#103:MapSortAggregate\ninput=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel106
> [label="rel#106:Exchange\ninput=RelSubset#104,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel108
> [label="rel#108:MapSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset104 [label="rel#104:RelSubset#9.IGNITE.random.[]"]
> subset105 [label="rel#105:RelSubset#9.IGNITE.single.[]"]
> }
> root -> subset91;
> subset77 -> rel1;
> subset79 -> rel78; rel78 -> subset77;
> subset80 -> rel2;
> subset82 -> rel81; rel81 -> subset80;
> subset84 -> rel83; rel83 -> subset82;
> subset86 -> rel85; rel85 -> subset84;
> subset88 -> rel87; rel87 -> subset79[label="0"]; rel87 -> subset86[label="1"];
> subset90 -> rel89; rel89 -> subset88;
> subset91 -> rel93; rel93 -> subset92;
> subset91 -> rel98; rel98 -> subset96;
> subset91 -> rel101; rel101 -> subset92;
> subset91 -> rel107; rel107 -> subset105;
> subset95 -> rel94; rel94 -> subset88;
> subset96 -> rel97; rel97 -> subset95;
> subset96 -> rel99; rel99 -> subset92;
> subset104 -> rel103; rel103 -> subset102;
> subset105 -> rel106; rel106 -> subset104;
> subset105 -> rel108; rel108 -> subset92;
> }
> at
> org.apache.ignite.internal.sql.api.IgniteSqlImpl.lambda$executeAsyncInternal$5(IgniteSqlImpl.java:379)
> at
> java.base/java.util.concurrent.CompletableFuture.uniExceptionally(CompletableFuture.java:986)
> at
> java.base/java.util.concurrent.CompletableFuture$UniExceptionally.tryFire(CompletableFuture.java:970)
> at
> java.base/java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:506)
> at
> java.base/java.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1705)
> at
> java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
> at
> java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
> at java.base/java.lang.Thread.run(Thread.java:834)
> Caused by: org.apache.ignite.sql.SqlException: IGN-CMN-65535
> TraceId:c2961dbf-64fd-4806-a2d9-20368f8454e5 There are not enough rules to
> produce a node with desired properties: convention=IGNITE, distr=single,
> sort=[].
> Missing conversions are LogicalRepeatUnion[convention: NONE -> IGNITE, distr:
> any -> single], LogicalRepeatUnion[convention: NONE -> IGNITE, distr: any ->
> random]
> There are 2 empty subsets:
> Empty subset 0: rel#92:RelSubset#6.IGNITE.single.[], the relevant part of the
> original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Empty subset 1: rel#102:RelSubset#6.IGNITE.random.[], the relevant part of
> the original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Root: rel#91:RelSubset#7.IGNITE.single.[]
> Original rel:
> LogicalAggregate(subset=[rel#91:RelSubset#7.IGNITE.single.[]], group=[{}],
> EXPR$0=[SUM($0)]): rowcount = 1.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 89
> LogicalRepeatUnion(subset=[rel#88:RelSubset#6.NONE.any.[]], all=[true]):
> rowcount = 501.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 87
> LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 1.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 78
> LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]]): rowcount = 1.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 1
> LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 50.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 85
> LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)]): rowcount = 50.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 83
> LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)]): rowcount = 50.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 81
> LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]]): rowcount = 100.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 2
> Sets:
> Set#0, type: RecordType(INTEGER EXPR$0)
> rel#77:RelSubset#0.NONE.any.[0], best=null
> rel#1:LogicalValues.NONE.any.[0](type=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]), rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity]
> Set#1, type: RecordType(INTEGER EXPR$0)
> rel#79:RelSubset#1.NONE.any.[0], best=null
> rel#78:LogicalTableSpool.NONE.any.[0](input=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#2, type: RecordType(INTEGER N)
> rel#80:RelSubset#2.NONE.any.[], best=null
> rel#2:LogicalTableScan.NONE.any.[](table=[T]), rowcount=100.0, cumulative
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]
> Set#3, type: RecordType(INTEGER N)
> rel#82:RelSubset#3.NONE.any.[], best=null
> rel#81:LogicalFilter.NONE.any.[](input=RelSubset#80,condition=<($0, 100)),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#4, type: RecordType(INTEGER EXPR$0)
> rel#84:RelSubset#4.NONE.any.[], best=null
> rel#83:LogicalProject.NONE.any.[](input=RelSubset#82,exprs=[+($0, 1)]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#5, type: RecordType(INTEGER EXPR$0)
> rel#86:RelSubset#5.NONE.any.[], best=null
> rel#85:LogicalTableSpool.NONE.any.[](input=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#6, type: RecordType(INTEGER EXPR$0)
> rel#88:RelSubset#6.NONE.any.[], best=null
> rel#87:LogicalRepeatUnion.NONE.any.[](left=RelSubset#79,right=RelSubset#86,all=true),
> rowcount=501.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#92:RelSubset#6.IGNITE.single.[], best=null
> rel#102:RelSubset#6.IGNITE.random.[], best=null
> Set#7, type: RecordType(BIGINT EXPR$0)
> rel#90:RelSubset#7.NONE.any.[], best=null
> rel#89:LogicalAggregate.NONE.any.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#91:RelSubset#7.IGNITE.single.[], best=null
> rel#93:ColocatedHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#98:ReduceHashAggregate.IGNITE.single.[](input=RelSubset#96,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#101:ColocatedSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#107:ReduceSortAggregate.IGNITE.single.[](input=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#8, type: RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)
> rel#95:RelSubset#8.IGNITE.random.[], best=null
> rel#94:MapHashAggregate.IGNITE.random.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#96:RelSubset#8.IGNITE.single.[], best=null
> rel#97:Exchange.IGNITE.single.[](input=RelSubset#95,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#99:MapHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#9, type: RecordType(DECIMAL(32767, 0) _ACC0)
> rel#104:RelSubset#9.IGNITE.random.[], best=null
> rel#103:MapSortAggregate.IGNITE.random.[](input=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#105:RelSubset#9.IGNITE.single.[], best=null
> rel#106:Exchange.IGNITE.single.[](input=RelSubset#104,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#108:MapSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Graphviz:
> digraph G {
> root [style=filled,label="Root"];
> subgraph cluster0{
> label="Set 0 RecordType(INTEGER EXPR$0)";
> rel1 [label="rel#1:LogicalValues\ntype=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]\nrows=1.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset77 [label="rel#77:RelSubset#0.NONE.any.[0]"]
> }
> subgraph cluster1{
> label="Set 1 RecordType(INTEGER EXPR$0)";
> rel78
> [label="rel#78:LogicalTableSpool\ninput=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset79 [label="rel#79:RelSubset#1.NONE.any.[0]"]
> }
> subgraph cluster2{
> label="Set 2 RecordType(INTEGER N)";
> rel2 [label="rel#2:LogicalTableScan\ntable=[T]\nrows=100.0, cost=IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity]",shape=box]
> subset80 [label="rel#80:RelSubset#2.NONE.any.[]"]
> }
> subgraph cluster3{
> label="Set 3 RecordType(INTEGER N)";
> rel81 [label="rel#81:LogicalFilter\ninput=RelSubset#80,condition=<($0,
> 100)\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset82 [label="rel#82:RelSubset#3.NONE.any.[]"]
> }
> subgraph cluster4{
> label="Set 4 RecordType(INTEGER EXPR$0)";
> rel83 [label="rel#83:LogicalProject\ninput=RelSubset#82,exprs=[+($0,
> 1)]\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset84 [label="rel#84:RelSubset#4.NONE.any.[]"]
> }
> subgraph cluster5{
> label="Set 5 RecordType(INTEGER EXPR$0)";
> rel85
> [label="rel#85:LogicalTableSpool\ninput=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]\nrows=50.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset86 [label="rel#86:RelSubset#5.NONE.any.[]"]
> }
> subgraph cluster6{
> label="Set 6 RecordType(INTEGER EXPR$0)";
> rel87
> [label="rel#87:LogicalRepeatUnion\nleft=RelSubset#79,right=RelSubset#86,all=true\nrows=501.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset88 [label="rel#88:RelSubset#6.NONE.any.[]"]
> subset92 [label="rel#92:RelSubset#6.IGNITE.single.[]",color=red]
> subset102 [label="rel#102:RelSubset#6.IGNITE.random.[]",color=red]
> }
> subgraph cluster7{
> label="Set 7 RecordType(BIGINT EXPR$0)";
> rel89
> [label="rel#89:LogicalAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel93
> [label="rel#93:ColocatedHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel98
> [label="rel#98:ReduceHashAggregate\ninput=RelSubset#96,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel101
> [label="rel#101:ColocatedSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel107
> [label="rel#107:ReduceSortAggregate\ninput=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset90 [label="rel#90:RelSubset#7.NONE.any.[]"]
> subset91 [label="rel#91:RelSubset#7.IGNITE.single.[]"]
> }
> subgraph cluster8{
> label="Set 8 RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)";
> rel94
> [label="rel#94:MapHashAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel97
> [label="rel#97:Exchange\ninput=RelSubset#95,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel99
> [label="rel#99:MapHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset95 [label="rel#95:RelSubset#8.IGNITE.random.[]"]
> subset96 [label="rel#96:RelSubset#8.IGNITE.single.[]"]
> }
> subgraph cluster9{
> label="Set 9 RecordType(DECIMAL(32767, 0) _ACC0)";
> rel103
> [label="rel#103:MapSortAggregate\ninput=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel106
> [label="rel#106:Exchange\ninput=RelSubset#104,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel108
> [label="rel#108:MapSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset104 [label="rel#104:RelSubset#9.IGNITE.random.[]"]
> subset105 [label="rel#105:RelSubset#9.IGNITE.single.[]"]
> }
> root -> subset91;
> subset77 -> rel1;
> subset79 -> rel78; rel78 -> subset77;
> subset80 -> rel2;
> subset82 -> rel81; rel81 -> subset80;
> subset84 -> rel83; rel83 -> subset82;
> subset86 -> rel85; rel85 -> subset84;
> subset88 -> rel87; rel87 -> subset79[label="0"]; rel87 -> subset86[label="1"];
> subset90 -> rel89; rel89 -> subset88;
> subset91 -> rel93; rel93 -> subset92;
> subset91 -> rel98; rel98 -> subset96;
> subset91 -> rel101; rel101 -> subset92;
> subset91 -> rel107; rel107 -> subset105;
> subset95 -> rel94; rel94 -> subset88;
> subset96 -> rel97; rel97 -> subset95;
> subset96 -> rel99; rel99 -> subset92;
> subset104 -> rel103; rel103 -> subset102;
> subset105 -> rel106; rel106 -> subset104;
> subset105 -> rel108; rel108 -> subset92;
> }
> at
> org.apache.ignite.internal.lang.SqlExceptionMapperUtil.mapToPublicSqlException(SqlExceptionMapperUtil.java:61)
> at
> org.apache.ignite.internal.sql.engine.prepare.PrepareServiceImpl.lambda$prepareAsync$0(PrepareServiceImpl.java:246)
> ... 7 more
> Caused by: org.apache.ignite.lang.IgniteException: IGN-CMN-65535
> TraceId:c2961dbf-64fd-4806-a2d9-20368f8454e5 There are not enough rules to
> produce a node with desired properties: convention=IGNITE, distr=single,
> sort=[].
> Missing conversions are LogicalRepeatUnion[convention: NONE -> IGNITE, distr:
> any -> single], LogicalRepeatUnion[convention: NONE -> IGNITE, distr: any ->
> random]
> There are 2 empty subsets:
> Empty subset 0: rel#92:RelSubset#6.IGNITE.single.[], the relevant part of the
> original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Empty subset 1: rel#102:RelSubset#6.IGNITE.random.[], the relevant part of
> the original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Root: rel#91:RelSubset#7.IGNITE.single.[]
> Original rel:
> LogicalAggregate(subset=[rel#91:RelSubset#7.IGNITE.single.[]], group=[{}],
> EXPR$0=[SUM($0)]): rowcount = 1.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 89
> LogicalRepeatUnion(subset=[rel#88:RelSubset#6.NONE.any.[]], all=[true]):
> rowcount = 501.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 87
> LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 1.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 78
> LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]]): rowcount = 1.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 1
> LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 50.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 85
> LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)]): rowcount = 50.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 83
> LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)]): rowcount = 50.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 81
> LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]]): rowcount = 100.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 2
> Sets:
> Set#0, type: RecordType(INTEGER EXPR$0)
> rel#77:RelSubset#0.NONE.any.[0], best=null
> rel#1:LogicalValues.NONE.any.[0](type=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]), rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity]
> Set#1, type: RecordType(INTEGER EXPR$0)
> rel#79:RelSubset#1.NONE.any.[0], best=null
> rel#78:LogicalTableSpool.NONE.any.[0](input=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#2, type: RecordType(INTEGER N)
> rel#80:RelSubset#2.NONE.any.[], best=null
> rel#2:LogicalTableScan.NONE.any.[](table=[T]), rowcount=100.0, cumulative
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]
> Set#3, type: RecordType(INTEGER N)
> rel#82:RelSubset#3.NONE.any.[], best=null
> rel#81:LogicalFilter.NONE.any.[](input=RelSubset#80,condition=<($0, 100)),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#4, type: RecordType(INTEGER EXPR$0)
> rel#84:RelSubset#4.NONE.any.[], best=null
> rel#83:LogicalProject.NONE.any.[](input=RelSubset#82,exprs=[+($0, 1)]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#5, type: RecordType(INTEGER EXPR$0)
> rel#86:RelSubset#5.NONE.any.[], best=null
> rel#85:LogicalTableSpool.NONE.any.[](input=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#6, type: RecordType(INTEGER EXPR$0)
> rel#88:RelSubset#6.NONE.any.[], best=null
> rel#87:LogicalRepeatUnion.NONE.any.[](left=RelSubset#79,right=RelSubset#86,all=true),
> rowcount=501.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#92:RelSubset#6.IGNITE.single.[], best=null
> rel#102:RelSubset#6.IGNITE.random.[], best=null
> Set#7, type: RecordType(BIGINT EXPR$0)
> rel#90:RelSubset#7.NONE.any.[], best=null
> rel#89:LogicalAggregate.NONE.any.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#91:RelSubset#7.IGNITE.single.[], best=null
> rel#93:ColocatedHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#98:ReduceHashAggregate.IGNITE.single.[](input=RelSubset#96,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#101:ColocatedSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#107:ReduceSortAggregate.IGNITE.single.[](input=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#8, type: RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)
> rel#95:RelSubset#8.IGNITE.random.[], best=null
> rel#94:MapHashAggregate.IGNITE.random.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#96:RelSubset#8.IGNITE.single.[], best=null
> rel#97:Exchange.IGNITE.single.[](input=RelSubset#95,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#99:MapHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#9, type: RecordType(DECIMAL(32767, 0) _ACC0)
> rel#104:RelSubset#9.IGNITE.random.[], best=null
> rel#103:MapSortAggregate.IGNITE.random.[](input=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#105:RelSubset#9.IGNITE.single.[], best=null
> rel#106:Exchange.IGNITE.single.[](input=RelSubset#104,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#108:MapSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Graphviz:
> digraph G {
> root [style=filled,label="Root"];
> subgraph cluster0{
> label="Set 0 RecordType(INTEGER EXPR$0)";
> rel1 [label="rel#1:LogicalValues\ntype=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]\nrows=1.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset77 [label="rel#77:RelSubset#0.NONE.any.[0]"]
> }
> subgraph cluster1{
> label="Set 1 RecordType(INTEGER EXPR$0)";
> rel78
> [label="rel#78:LogicalTableSpool\ninput=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset79 [label="rel#79:RelSubset#1.NONE.any.[0]"]
> }
> subgraph cluster2{
> label="Set 2 RecordType(INTEGER N)";
> rel2 [label="rel#2:LogicalTableScan\ntable=[T]\nrows=100.0, cost=IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity]",shape=box]
> subset80 [label="rel#80:RelSubset#2.NONE.any.[]"]
> }
> subgraph cluster3{
> label="Set 3 RecordType(INTEGER N)";
> rel81 [label="rel#81:LogicalFilter\ninput=RelSubset#80,condition=<($0,
> 100)\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset82 [label="rel#82:RelSubset#3.NONE.any.[]"]
> }
> subgraph cluster4{
> label="Set 4 RecordType(INTEGER EXPR$0)";
> rel83 [label="rel#83:LogicalProject\ninput=RelSubset#82,exprs=[+($0,
> 1)]\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset84 [label="rel#84:RelSubset#4.NONE.any.[]"]
> }
> subgraph cluster5{
> label="Set 5 RecordType(INTEGER EXPR$0)";
> rel85
> [label="rel#85:LogicalTableSpool\ninput=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]\nrows=50.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset86 [label="rel#86:RelSubset#5.NONE.any.[]"]
> }
> subgraph cluster6{
> label="Set 6 RecordType(INTEGER EXPR$0)";
> rel87
> [label="rel#87:LogicalRepeatUnion\nleft=RelSubset#79,right=RelSubset#86,all=true\nrows=501.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset88 [label="rel#88:RelSubset#6.NONE.any.[]"]
> subset92 [label="rel#92:RelSubset#6.IGNITE.single.[]",color=red]
> subset102 [label="rel#102:RelSubset#6.IGNITE.random.[]",color=red]
> }
> subgraph cluster7{
> label="Set 7 RecordType(BIGINT EXPR$0)";
> rel89
> [label="rel#89:LogicalAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel93
> [label="rel#93:ColocatedHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel98
> [label="rel#98:ReduceHashAggregate\ninput=RelSubset#96,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel101
> [label="rel#101:ColocatedSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel107
> [label="rel#107:ReduceSortAggregate\ninput=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset90 [label="rel#90:RelSubset#7.NONE.any.[]"]
> subset91 [label="rel#91:RelSubset#7.IGNITE.single.[]"]
> }
> subgraph cluster8{
> label="Set 8 RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)";
> rel94
> [label="rel#94:MapHashAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel97
> [label="rel#97:Exchange\ninput=RelSubset#95,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel99
> [label="rel#99:MapHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset95 [label="rel#95:RelSubset#8.IGNITE.random.[]"]
> subset96 [label="rel#96:RelSubset#8.IGNITE.single.[]"]
> }
> subgraph cluster9{
> label="Set 9 RecordType(DECIMAL(32767, 0) _ACC0)";
> rel103
> [label="rel#103:MapSortAggregate\ninput=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel106
> [label="rel#106:Exchange\ninput=RelSubset#104,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel108
> [label="rel#108:MapSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset104 [label="rel#104:RelSubset#9.IGNITE.random.[]"]
> subset105 [label="rel#105:RelSubset#9.IGNITE.single.[]"]
> }
> root -> subset91;
> subset77 -> rel1;
> subset79 -> rel78; rel78 -> subset77;
> subset80 -> rel2;
> subset82 -> rel81; rel81 -> subset80;
> subset84 -> rel83; rel83 -> subset82;
> subset86 -> rel85; rel85 -> subset84;
> subset88 -> rel87; rel87 -> subset79[label="0"]; rel87 -> subset86[label="1"];
> subset90 -> rel89; rel89 -> subset88;
> subset91 -> rel93; rel93 -> subset92;
> subset91 -> rel98; rel98 -> subset96;
> subset91 -> rel101; rel101 -> subset92;
> subset91 -> rel107; rel107 -> subset105;
> subset95 -> rel94; rel94 -> subset88;
> subset96 -> rel97; rel97 -> subset95;
> subset96 -> rel99; rel99 -> subset92;
> subset104 -> rel103; rel103 -> subset102;
> subset105 -> rel106; rel106 -> subset104;
> subset105 -> rel108; rel108 -> subset92;
> }
> at
> org.apache.ignite.internal.lang.IgniteExceptionMapperUtil.mapToPublicException(IgniteExceptionMapperUtil.java:118)
> at
> org.apache.ignite.internal.lang.SqlExceptionMapperUtil.mapToPublicSqlException(SqlExceptionMapperUtil.java:51)
> ... 8 more
> Caused by: org.apache.calcite.plan.RelOptPlanner$CannotPlanException: There
> are not enough rules to produce a node with desired properties:
> convention=IGNITE, distr=single, sort=[].
> Missing conversions are LogicalRepeatUnion[convention: NONE -> IGNITE, distr:
> any -> single], LogicalRepeatUnion[convention: NONE -> IGNITE, distr: any ->
> random]
> There are 2 empty subsets:
> Empty subset 0: rel#92:RelSubset#6.IGNITE.single.[], the relevant part of the
> original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Empty subset 1: rel#102:RelSubset#6.IGNITE.random.[], the relevant part of
> the original plan is as follows
> 87:LogicalRepeatUnion(all=[true])
> 78:LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 1:LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]])
> 85:LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]])
> 83:LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)])
> 81:LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)])
> 2:LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]])
> Root: rel#91:RelSubset#7.IGNITE.single.[]
> Original rel:
> LogicalAggregate(subset=[rel#91:RelSubset#7.IGNITE.single.[]], group=[{}],
> EXPR$0=[SUM($0)]): rowcount = 1.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 89
> LogicalRepeatUnion(subset=[rel#88:RelSubset#6.NONE.any.[]], all=[true]):
> rowcount = 501.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 87
> LogicalTableSpool(subset=[rel#79:RelSubset#1.NONE.any.[0]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 1.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 78
> LogicalValues(subset=[rel#77:RelSubset#0.NONE.any.[0]], tuples=[[{ 1
> }]]): rowcount = 1.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 1
> LogicalTableSpool(subset=[rel#86:RelSubset#5.NONE.any.[]],
> readType=[LAZY], writeType=[LAZY], table=[[T]]): rowcount = 50.0, cumulative
> cost = IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity], id = 85
> LogicalProject(subset=[rel#84:RelSubset#4.NONE.any.[]], EXPR$0=[+($0,
> 1)]): rowcount = 50.0, cumulative cost = IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity], id = 83
> LogicalFilter(subset=[rel#82:RelSubset#3.NONE.any.[]],
> condition=[<($0, 100)]): rowcount = 50.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 81
> LogicalTableScan(subset=[rel#80:RelSubset#2.NONE.any.[]],
> table=[[T]]): rowcount = 100.0, cumulative cost = IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity], id = 2
> Sets:
> Set#0, type: RecordType(INTEGER EXPR$0)
> rel#77:RelSubset#0.NONE.any.[0], best=null
> rel#1:LogicalValues.NONE.any.[0](type=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]), rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity,
> cpu=Infinity, memory=Infinity, io=Infinity, network=Infinity]
> Set#1, type: RecordType(INTEGER EXPR$0)
> rel#79:RelSubset#1.NONE.any.[0], best=null
> rel#78:LogicalTableSpool.NONE.any.[0](input=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#2, type: RecordType(INTEGER N)
> rel#80:RelSubset#2.NONE.any.[], best=null
> rel#2:LogicalTableScan.NONE.any.[](table=[T]), rowcount=100.0, cumulative
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]
> Set#3, type: RecordType(INTEGER N)
> rel#82:RelSubset#3.NONE.any.[], best=null
> rel#81:LogicalFilter.NONE.any.[](input=RelSubset#80,condition=<($0, 100)),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#4, type: RecordType(INTEGER EXPR$0)
> rel#84:RelSubset#4.NONE.any.[], best=null
> rel#83:LogicalProject.NONE.any.[](input=RelSubset#82,exprs=[+($0, 1)]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#5, type: RecordType(INTEGER EXPR$0)
> rel#86:RelSubset#5.NONE.any.[], best=null
> rel#85:LogicalTableSpool.NONE.any.[](input=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]),
> rowcount=50.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#6, type: RecordType(INTEGER EXPR$0)
> rel#88:RelSubset#6.NONE.any.[], best=null
> rel#87:LogicalRepeatUnion.NONE.any.[](left=RelSubset#79,right=RelSubset#86,all=true),
> rowcount=501.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#92:RelSubset#6.IGNITE.single.[], best=null
> rel#102:RelSubset#6.IGNITE.random.[], best=null
> Set#7, type: RecordType(BIGINT EXPR$0)
> rel#90:RelSubset#7.NONE.any.[], best=null
> rel#89:LogicalAggregate.NONE.any.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#91:RelSubset#7.IGNITE.single.[], best=null
> rel#93:ColocatedHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#98:ReduceHashAggregate.IGNITE.single.[](input=RelSubset#96,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#101:ColocatedSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#107:ReduceSortAggregate.IGNITE.single.[](input=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#8, type: RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)
> rel#95:RelSubset#8.IGNITE.random.[], best=null
> rel#94:MapHashAggregate.IGNITE.random.[](input=RelSubset#88,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#96:RelSubset#8.IGNITE.single.[], best=null
> rel#97:Exchange.IGNITE.single.[](input=RelSubset#95,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#99:MapHashAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0)),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Set#9, type: RecordType(DECIMAL(32767, 0) _ACC0)
> rel#104:RelSubset#9.IGNITE.random.[], best=null
> rel#103:MapSortAggregate.IGNITE.random.[](input=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#105:RelSubset#9.IGNITE.single.[], best=null
> rel#106:Exchange.IGNITE.single.[](input=RelSubset#104,distribution=single),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> rel#108:MapSortAggregate.IGNITE.single.[](input=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]),
> rowcount=1.0, cumulative cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]
> Graphviz:
> digraph G {
> root [style=filled,label="Root"];
> subgraph cluster0{
> label="Set 0 RecordType(INTEGER EXPR$0)";
> rel1 [label="rel#1:LogicalValues\ntype=RecordType(INTEGER EXPR$0),tuples=[{ 1
> }]\nrows=1.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset77 [label="rel#77:RelSubset#0.NONE.any.[0]"]
> }
> subgraph cluster1{
> label="Set 1 RecordType(INTEGER EXPR$0)";
> rel78
> [label="rel#78:LogicalTableSpool\ninput=RelSubset#77,readType=LAZY,writeType=LAZY,table=[T]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset79 [label="rel#79:RelSubset#1.NONE.any.[0]"]
> }
> subgraph cluster2{
> label="Set 2 RecordType(INTEGER N)";
> rel2 [label="rel#2:LogicalTableScan\ntable=[T]\nrows=100.0, cost=IgniteCost
> [rowCount=Infinity, cpu=Infinity, memory=Infinity, io=Infinity,
> network=Infinity]",shape=box]
> subset80 [label="rel#80:RelSubset#2.NONE.any.[]"]
> }
> subgraph cluster3{
> label="Set 3 RecordType(INTEGER N)";
> rel81 [label="rel#81:LogicalFilter\ninput=RelSubset#80,condition=<($0,
> 100)\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset82 [label="rel#82:RelSubset#3.NONE.any.[]"]
> }
> subgraph cluster4{
> label="Set 4 RecordType(INTEGER EXPR$0)";
> rel83 [label="rel#83:LogicalProject\ninput=RelSubset#82,exprs=[+($0,
> 1)]\nrows=50.0, cost=IgniteCost [rowCount=Infinity, cpu=Infinity,
> memory=Infinity, io=Infinity, network=Infinity]",shape=box]
> subset84 [label="rel#84:RelSubset#4.NONE.any.[]"]
> }
> subgraph cluster5{
> label="Set 5 RecordType(INTEGER EXPR$0)";
> rel85
> [label="rel#85:LogicalTableSpool\ninput=RelSubset#84,readType=LAZY,writeType=LAZY,table=[T]\nrows=50.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset86 [label="rel#86:RelSubset#5.NONE.any.[]"]
> }
> subgraph cluster6{
> label="Set 6 RecordType(INTEGER EXPR$0)";
> rel87
> [label="rel#87:LogicalRepeatUnion\nleft=RelSubset#79,right=RelSubset#86,all=true\nrows=501.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset88 [label="rel#88:RelSubset#6.NONE.any.[]"]
> subset92 [label="rel#92:RelSubset#6.IGNITE.single.[]",color=red]
> subset102 [label="rel#102:RelSubset#6.IGNITE.random.[]",color=red]
> }
> subgraph cluster7{
> label="Set 7 RecordType(BIGINT EXPR$0)";
> rel89
> [label="rel#89:LogicalAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel93
> [label="rel#93:ColocatedHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel98
> [label="rel#98:ReduceHashAggregate\ninput=RelSubset#96,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel101
> [label="rel#101:ColocatedSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel107
> [label="rel#107:ReduceSortAggregate\ninput=RelSubset#105,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset90 [label="rel#90:RelSubset#7.NONE.any.[]"]
> subset91 [label="rel#91:RelSubset#7.IGNITE.single.[]"]
> }
> subgraph cluster8{
> label="Set 8 RecordType(DECIMAL(32767, 0) _ACC0, TINYINT _GROUP_ID)";
> rel94
> [label="rel#94:MapHashAggregate\ninput=RelSubset#88,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel97
> [label="rel#97:Exchange\ninput=RelSubset#95,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel99
> [label="rel#99:MapHashAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0)\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset95 [label="rel#95:RelSubset#8.IGNITE.random.[]"]
> subset96 [label="rel#96:RelSubset#8.IGNITE.single.[]"]
> }
> subgraph cluster9{
> label="Set 9 RecordType(DECIMAL(32767, 0) _ACC0)";
> rel103
> [label="rel#103:MapSortAggregate\ninput=RelSubset#102,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel106
> [label="rel#106:Exchange\ninput=RelSubset#104,distribution=single\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> rel108
> [label="rel#108:MapSortAggregate\ninput=RelSubset#92,group={},EXPR$0=SUM($0),collation=[]\nrows=1.0,
> cost=IgniteCost [rowCount=Infinity, cpu=Infinity, memory=Infinity,
> io=Infinity, network=Infinity]",shape=box]
> subset104 [label="rel#104:RelSubset#9.IGNITE.random.[]"]
> subset105 [label="rel#105:RelSubset#9.IGNITE.single.[]"]
> }
> root -> subset91;
> subset77 -> rel1;
> subset79 -> rel78; rel78 -> subset77;
> subset80 -> rel2;
> subset82 -> rel81; rel81 -> subset80;
> subset84 -> rel83; rel83 -> subset82;
> subset86 -> rel85; rel85 -> subset84;
> subset88 -> rel87; rel87 -> subset79[label="0"]; rel87 -> subset86[label="1"];
> subset90 -> rel89; rel89 -> subset88;
> subset91 -> rel93; rel93 -> subset92;
> subset91 -> rel98; rel98 -> subset96;
> subset91 -> rel101; rel101 -> subset92;
> subset91 -> rel107; rel107 -> subset105;
> subset95 -> rel94; rel94 -> subset88;
> subset96 -> rel97; rel97 -> subset95;
> subset96 -> rel99; rel99 -> subset92;
> subset104 -> rel103; rel103 -> subset102;
> subset105 -> rel106; rel106 -> subset104;
> subset105 -> rel108; rel108 -> subset92;
> }
> at
> org.apache.calcite.plan.volcano.RelSubset$CheapestPlanReplacer.visit(RelSubset.java:718)
> at
> org.apache.calcite.plan.volcano.RelSubset.buildCheapestPlan(RelSubset.java:391)
> at
> org.apache.calcite.plan.volcano.VolcanoPlanner.findBestExp(VolcanoPlanner.java:534)
> at org.apache.calcite.tools.Programs$RuleSetProgram.run(Programs.java:328)
> at
> org.apache.ignite.internal.sql.engine.prepare.IgnitePlanner.transform(IgnitePlanner.java:386)
> at
> org.apache.ignite.internal.sql.engine.prepare.PlannerHelper.optimize(PlannerHelper.java:140)
> at
> org.apache.ignite.internal.sql.engine.prepare.PrepareServiceImpl.doOptimize(PrepareServiceImpl.java:578)
> at
> org.apache.ignite.internal.sql.engine.prepare.PrepareServiceImpl.lambda$prepareQuery$3(PrepareServiceImpl.java:359)
> at
> java.base/java.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1700)
> ... 3 more
> {noformat}
> Let's handle recursive CTE with something like
> {noformat}
> Recursive CTE are not supported.
> {noformat}
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