Javelin2007 opened a new issue, #2708:
URL: https://github.com/apache/drill/issues/2708
1. The version information is as follows:
apache drill> select commit_message, commit_time from sys.version;
+----------------------------------------------------------------------------------+---------------------------+
| commit_message
| commit_time |
+----------------------------------------------------------------------------------+---------------------------+
| DRILL-8357: Add new config options to the Splunk storage plugin (extra
docs) (#2706) | 15.11.2022 @ 20:34:55 CST |
+----------------------------------------------------------------------------------+---------------------------+
1 row selected (0.133 seconds)
2. Create tables and insert data in postgresql and mysql database
create table t1(col1 int);
insert into t1 values(1), (2);
3. Create mysql and postgresql Plugins in Storage label via
http://localhost:8047/storage pages
4. The sql statement is executed as follows
apache drill> select * from pgsql.t1 as pg ,my.test.t1 as my where pg.col1
= my.col1 and pg.col1 = 1 and my.col1 =1 ;
Error: UNSUPPORTED_OPERATION ERROR: This query cannot be planned possibly
due to either a cartesian join or an inequality join.
If a cartesian or inequality join is used intentionally, set the option
'planner.enable_nljoin_for_scalar_only' to false and try again.
[Error Id: 91583f69-5aa6-44d5-a29c-63e2358932ea ] (state=,code=0)
apache drill> set planner.enable_nljoin_for_scalar_only=false;
+------+------------------------------------------------+
| ok | summary |
+------+------------------------------------------------+
| true | planner.enable_nljoin_for_scalar_only updated. |
+------+------------------------------------------------+
1 row selected (0.212 seconds)
apache drill> select * from pgsql.t1 as pg ,my.test.t1 as my where pg.col1
= my.col1 and pg.col1 = 1 and my.col1 =1 ;
Error: UNSUPPORTED_OPERATION ERROR: This query cannot be planned possibly
due to either a cartesian join or an inequality join.
If a cartesian or inequality join is used intentionally, set the option
'planner.enable_nljoin_for_scalar_only' to false and try again.
[Error Id: 37d0dbca-40d1-4de5-9443-b48ce3a172c0 ] (state=,code=0)
apache drill>
5. The below is drillbit.log info
2022-11-18 08:08:01,169 [1c88c29e-3040-efc1-46a6-33152cfbab32:foreman] INFO
o.a.drill.exec.work.foreman.Foreman - Query text for query with id
1c88c29e-3040-efc1-46a6-33152cfbab32 issued by test: select * from pgsql.t1 as
pg ,my.test.t1 as my where pg.col1 = my.col1 and pg.col1 = 1 and my.col1 =1
2022-11-18 08:08:01,258 [1c88c29e-3040-efc1-46a6-33152cfbab32:foreman] ERROR
o.a.d.e.p.s.h.DefaultSqlHandler - There are not enough rules to produce a node
with desired properties: convention=PHYSICAL,
DrillDistributionTraitDef=SINGLETON([]), sort=[].
Missing conversions are JdbcFilter[convention: JDBC.pgsql -> JDBC.my],
JdbcFilter[convention: JDBC.my -> JDBC.pgsql]
There are 2 empty subsets:
Empty subset 0: rel#15241:RelSubset#12.JDBC.my.ANY([]).[], the relevant part
of the original plan is as follows
15211:JdbcFilter(condition=[=($0, 1)])
14914:JdbcTableScan(subset=[rel#15210:RelSubset#11.JDBC.pgsql.ANY([]).[]],
table=[[pgsql, t1]])
Empty subset 1: rel#15247:RelSubset#15.JDBC.pgsql.ANY([]).[], the relevant
part of the original plan is as follows
15216:JdbcFilter(condition=[=($0, 1)])
14915:JdbcTableScan(subset=[rel#15215:RelSubset#14.JDBC.my.ANY([]).[]],
table=[[my, test, t1]])
Root: rel#15224:RelSubset#18.PHYSICAL.SINGLETON([]).[]
Original rel:
LogicalProject(subset=[rel#14956:RelSubset#4.LOGICAL.ANY([]).[]], col1=[$0],
col10=[$1]): rowcount = 3.375E15, cumulative cost = {3.375E15 rows, 6.75E15
cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 14954
LogicalFilter(subset=[rel#14953:RelSubset#3.NONE.ANY([]).[]],
condition=[AND(=($0, $1), =($0, 1), =($1, 1))]): rowcount = 3.375E15,
cumulative cost = {3.375E15 rows, 1.0E18 cpu, 0.0 io, 0.0 network, 0.0 memory},
id = 14952
LogicalJoin(subset=[rel#14951:RelSubset#2.NONE.ANY([]).[]],
condition=[true], joinType=[inner]): rowcount = 1.0E18, cumulative cost =
{1.0E18 rows, 0.0 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 14950
JdbcTableScan(subset=[rel#14948:RelSubset#0.JDBC.pgsql.ANY([]).[]],
table=[[pgsql, t1]]): rowcount = 1.0E9, cumulative cost = {100.0 rows, 101.0
cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 14914
JdbcTableScan(subset=[rel#14949:RelSubset#1.JDBC.my.ANY([]).[]],
table=[[my, test, t1]]): rowcount = 1.0E9, cumulative cost = {100.0 rows, 101.0
cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 14915
Sets:
Set#11, type: RecordType(INTEGER col1)
rel#15210:RelSubset#11.JDBC.pgsql.ANY([]).[], best=rel#14914
rel#14914:JdbcTableScan.JDBC.pgsql.ANY([]).[](table=[pgsql,
t1]), rowcount=1.0E9, cumulative cost={100.0 rows, 101.0 cpu, 0.0 io, 0.0
network, 0.0 memory}
rel#15227:RelSubset#11.LOGICAL.ANY([]).[], best=rel#15226
rel#15226:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15210),
rowcount=1.0E9, cumulative cost={1.000001E8 rows, 1.00000101E8 cpu, 0.0 io, 0.0
network, 0.0 memory}
rel#15254:RelSubset#11.PHYSICAL.SINGLETON([]).[], best=rel#15253
rel#15253:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15210),
rowcount=1.0E9, cumulative cost={1.0000001E9 rows, 1.000000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
Set#12, type: RecordType(INTEGER col1)
rel#15212:RelSubset#12.JDBC.pgsql.ANY([]).[], best=rel#15211
rel#15211:JdbcFilter.JDBC.pgsql.ANY([]).[](input=RelSubset#15210,condition==($0,
1)), rowcount=1.5E8, cumulative cost={1.500001E8 rows, 1.000000101E9 cpu, 0.0
io, 0.0 network, 0.0 memory}
rel#15246:AbstractConverter.JDBC.pgsql.ANY([]).[](input=RelSubset#15232,convention=JDBC.pgsql,DrillDistributionTraitDef=ANY([]),sort=[]),
rowcount=1.5E8, cumulative cost={inf}
rel#15230:RelSubset#12.LOGICAL.ANY([]).[], best=rel#15213
rel#15213:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15212),
rowcount=1.5E8, cumulative cost={1.650001E8 rows, 1.015000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
rel#15258:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15241),
rowcount=1.5E8, cumulative cost={inf}
rel#15232:RelSubset#12.PHYSICAL.SINGLETON([]).[], best=rel#15231
rel#15231:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15212),
rowcount=1.5E8, cumulative cost={3.000001E8 rows, 1.150000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
rel#15271:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15241),
rowcount=1.5E8, cumulative cost={inf}
rel#15241:RelSubset#12.JDBC.my.ANY([]).[], best=null
rel#15242:AbstractConverter.JDBC.my.ANY([]).[](input=RelSubset#15232,convention=JDBC.my,DrillDistributionTraitDef=ANY([]),sort=[]),
rowcount=1.5E8, cumulative cost={inf}
Set#14, type: RecordType(INTEGER col1)
rel#15215:RelSubset#14.JDBC.my.ANY([]).[], best=rel#14915
rel#14915:JdbcTableScan.JDBC.my.ANY([]).[](table=[my, test,
t1]), rowcount=1.0E9, cumulative cost={100.0 rows, 101.0 cpu, 0.0 io, 0.0
network, 0.0 memory}
rel#15235:RelSubset#14.LOGICAL.ANY([]).[], best=rel#15234
rel#15234:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15215),
rowcount=1.0E9, cumulative cost={1.000001E8 rows, 1.00000101E8 cpu, 0.0 io, 0.0
network, 0.0 memory}
rel#15256:RelSubset#14.PHYSICAL.SINGLETON([]).[], best=rel#15255
rel#15255:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15215),
rowcount=1.0E9, cumulative cost={1.0000001E9 rows, 1.000000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
Set#15, type: RecordType(INTEGER col1)
rel#15217:RelSubset#15.JDBC.my.ANY([]).[], best=rel#15216
rel#15216:JdbcFilter.JDBC.my.ANY([]).[](input=RelSubset#15215,condition==($0,
1)), rowcount=1.5E8, cumulative cost={1.500001E8 rows, 1.000000101E9 cpu, 0.0
io, 0.0 network, 0.0 memory}
rel#15243:AbstractConverter.JDBC.my.ANY([]).[](input=RelSubset#15240,convention=JDBC.my,DrillDistributionTraitDef=ANY([]),sort=[]),
rowcount=1.5E8, cumulative cost={inf}
rel#15238:RelSubset#15.LOGICAL.ANY([]).[], best=rel#15218
rel#15218:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15217),
rowcount=1.5E8, cumulative cost={1.650001E8 rows, 1.015000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
rel#15268:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15247),
rowcount=1.5E8, cumulative cost={inf}
rel#15240:RelSubset#15.PHYSICAL.SINGLETON([]).[], best=rel#15239
rel#15239:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15217),
rowcount=1.5E8, cumulative cost={3.000001E8 rows, 1.150000101E9 cpu, 0.0 io,
0.0 network, 0.0 memory}
rel#15274:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15247),
rowcount=1.5E8, cumulative cost={inf}
rel#15247:RelSubset#15.JDBC.pgsql.ANY([]).[], best=null
rel#15248:AbstractConverter.JDBC.pgsql.ANY([]).[](input=RelSubset#15240,convention=JDBC.pgsql,DrillDistributionTraitDef=ANY([]),sort=[]),
rowcount=1.5E8, cumulative cost={inf}
Set#17, type: RecordType(INTEGER col1, INTEGER col10)
rel#15221:RelSubset#17.LOGICAL.ANY([]).[], best=rel#15220
rel#15220:DrillJoinRel.LOGICAL.ANY([]).[](left=RelSubset#15230,right=RelSubset#15238,condition=true,joinType=inner),
rowcount=2.25E16, cumulative cost={6.300002E8 rows, 2.030000202E9 cpu, 0.0 io,
0.0 network, 1.32E9 memory}
rel#15263:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15245),
rowcount=2.25E16, cumulative cost={inf}
rel#15270:VertexDrel.LOGICAL.ANY([]).[](input=RelSubset#15250),
rowcount=2.25E16, cumulative cost={inf}
rel#15245:RelSubset#17.JDBC.my.ANY([]).[], best=null
rel#15244:JdbcJoin.JDBC.my.ANY([]).[](left=RelSubset#15241,right=RelSubset#15217,condition=true,joinType=inner),
rowcount=1.5E8, cumulative cost={inf}
rel#15250:RelSubset#17.JDBC.pgsql.ANY([]).[], best=null
rel#15249:JdbcJoin.JDBC.pgsql.ANY([]).[](left=RelSubset#15212,right=RelSubset#15247,condition=true,joinType=inner),
rowcount=1.5E8, cumulative cost={inf}
rel#15251:RelSubset#17.PHYSICAL.SINGLETON([]).[], best=null
rel#15273:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15245),
rowcount=2.25E16, cumulative cost={inf}
rel#15276:JdbcIntermediatePrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15250),
rowcount=2.25E16, cumulative cost={inf}
Set#18, type: RecordType(INTEGER col1, INTEGER col10)
rel#15223:RelSubset#18.LOGICAL.ANY([]).[], best=rel#15222
rel#15222:DrillScreenRel.LOGICAL.ANY([]).[](input=RelSubset#15221),
rowcount=2.25E16, cumulative cost={2.2500006300002E15 rows,
2.250002030000202E15 cpu, 0.0 io, 0.0 network, 1.32E9 memory}
rel#15224:RelSubset#18.PHYSICAL.SINGLETON([]).[], best=null
rel#15252:ScreenPrel.PHYSICAL.SINGLETON([]).[](input=RelSubset#15251),
rowcount=2.25E16, cumulative cost={inf}
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