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More scalar subqueries support #6372

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26 changes: 13 additions & 13 deletions benchmarks/expected-plans/q2.txt
Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,12 @@
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| logical_plan | Sort: supplier.s_acctbal DESC NULLS FIRST, nation.n_name ASC NULLS LAST, supplier.s_name ASC NULLS LAST, part.p_partkey ASC NULLS LAST |
| | Projection: supplier.s_acctbal, supplier.s_name, nation.n_name, part.p_partkey, part.p_mfgr, supplier.s_address, supplier.s_phone, supplier.s_comment |
| | Inner Join: partsupp.ps_supplycost = __scalar_sq_1.__value, part.p_partkey = __scalar_sq_1.ps_partkey |
| | Projection: part.p_partkey, part.p_mfgr, partsupp.ps_supplycost, supplier.s_name, supplier.s_address, supplier.s_phone, supplier.s_acctbal, supplier.s_comment, nation.n_name |
| | Inner Join: part.p_partkey = __scalar_sq_1.ps_partkey, partsupp.ps_supplycost = __scalar_sq_1.__value |
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The only difference in these plans appear to be the names / orders of the columns used internally -- the actual output and plan appear to be the same) 👍

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Yes

| | Projection: part.p_partkey, part.p_mfgr, supplier.s_name, supplier.s_address, supplier.s_phone, supplier.s_acctbal, supplier.s_comment, partsupp.ps_supplycost, nation.n_name |
| | Inner Join: nation.n_regionkey = region.r_regionkey |
| | Projection: part.p_partkey, part.p_mfgr, partsupp.ps_supplycost, supplier.s_name, supplier.s_address, supplier.s_phone, supplier.s_acctbal, supplier.s_comment, nation.n_name, nation.n_regionkey |
| | Projection: part.p_partkey, part.p_mfgr, supplier.s_name, supplier.s_address, supplier.s_phone, supplier.s_acctbal, supplier.s_comment, partsupp.ps_supplycost, nation.n_name, nation.n_regionkey |
| | Inner Join: supplier.s_nationkey = nation.n_nationkey |
| | Projection: part.p_partkey, part.p_mfgr, partsupp.ps_supplycost, supplier.s_name, supplier.s_address, supplier.s_nationkey, supplier.s_phone, supplier.s_acctbal, supplier.s_comment |
| | Projection: part.p_partkey, part.p_mfgr, supplier.s_name, supplier.s_address, supplier.s_nationkey, supplier.s_phone, supplier.s_acctbal, supplier.s_comment, partsupp.ps_supplycost |
| | Inner Join: partsupp.ps_suppkey = supplier.s_suppkey |
| | Projection: part.p_partkey, part.p_mfgr, partsupp.ps_suppkey, partsupp.ps_supplycost |
| | Inner Join: part.p_partkey = partsupp.ps_partkey |
Expand Down Expand Up @@ -38,22 +38,22 @@
| | TableScan: region projection=[r_regionkey, r_name] |
| physical_plan | SortPreservingMergeExec: [s_acctbal@0 DESC,n_name@2 ASC NULLS LAST,s_name@1 ASC NULLS LAST,p_partkey@3 ASC NULLS LAST] |
| | SortExec: expr=[s_acctbal@0 DESC,n_name@2 ASC NULLS LAST,s_name@1 ASC NULLS LAST,p_partkey@3 ASC NULLS LAST] |
| | ProjectionExec: expr=[s_acctbal@6 as s_acctbal, s_name@3 as s_name, n_name@8 as n_name, p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, s_address@4 as s_address, s_phone@5 as s_phone, s_comment@7 as s_comment] |
| | ProjectionExec: expr=[s_acctbal@5 as s_acctbal, s_name@2 as s_name, n_name@8 as n_name, p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, s_address@3 as s_address, s_phone@4 as s_phone, s_comment@6 as s_comment] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "ps_supplycost", index: 2 }, Column { name: "__value", index: 1 }), (Column { name: "p_partkey", index: 0 }, Column { name: "ps_partkey", index: 0 })] |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "p_partkey", index: 0 }, Column { name: "ps_partkey", index: 0 }), (Column { name: "ps_supplycost", index: 7 }, Column { name: "__value", index: 1 })] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | RepartitionExec: partitioning=Hash([Column { name: "ps_supplycost", index: 2 }, Column { name: "p_partkey", index: 0 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, ps_supplycost@2 as ps_supplycost, s_name@3 as s_name, s_address@4 as s_address, s_phone@5 as s_phone, s_acctbal@6 as s_acctbal, s_comment@7 as s_comment, n_name@8 as n_name] |
| | RepartitionExec: partitioning=Hash([Column { name: "p_partkey", index: 0 }, Column { name: "ps_supplycost", index: 7 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, s_name@2 as s_name, s_address@3 as s_address, s_phone@4 as s_phone, s_acctbal@5 as s_acctbal, s_comment@6 as s_comment, ps_supplycost@7 as ps_supplycost, n_name@8 as n_name] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "n_regionkey", index: 9 }, Column { name: "r_regionkey", index: 0 })] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | RepartitionExec: partitioning=Hash([Column { name: "n_regionkey", index: 9 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, ps_supplycost@2 as ps_supplycost, s_name@3 as s_name, s_address@4 as s_address, s_phone@6 as s_phone, s_acctbal@7 as s_acctbal, s_comment@8 as s_comment, n_name@10 as n_name, n_regionkey@11 as n_regionkey] |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, s_name@2 as s_name, s_address@3 as s_address, s_phone@5 as s_phone, s_acctbal@6 as s_acctbal, s_comment@7 as s_comment, ps_supplycost@8 as ps_supplycost, n_name@10 as n_name, n_regionkey@11 as n_regionkey] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "s_nationkey", index: 5 }, Column { name: "n_nationkey", index: 0 })] |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "s_nationkey", index: 4 }, Column { name: "n_nationkey", index: 0 })] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | RepartitionExec: partitioning=Hash([Column { name: "s_nationkey", index: 5 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, ps_supplycost@3 as ps_supplycost, s_name@5 as s_name, s_address@6 as s_address, s_nationkey@7 as s_nationkey, s_phone@8 as s_phone, s_acctbal@9 as s_acctbal, s_comment@10 as s_comment] |
| | RepartitionExec: partitioning=Hash([Column { name: "s_nationkey", index: 4 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[p_partkey@0 as p_partkey, p_mfgr@1 as p_mfgr, s_name@5 as s_name, s_address@6 as s_address, s_nationkey@7 as s_nationkey, s_phone@8 as s_phone, s_acctbal@9 as s_acctbal, s_comment@10 as s_comment, ps_supplycost@3 as ps_supplycost] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | HashJoinExec: mode=Partitioned, join_type=Inner, on=[(Column { name: "ps_suppkey", index: 2 }, Column { name: "s_suppkey", index: 0 })] |
| | CoalesceBatchesExec: target_batch_size=8192 |
Expand Down Expand Up @@ -85,7 +85,7 @@
| | FilterExec: r_name@1 = EUROPE |
| | MemoryExec: partitions=0, partition_sizes=[] |
| | CoalesceBatchesExec: target_batch_size=8192 |
| | RepartitionExec: partitioning=Hash([Column { name: "__value", index: 1 }, Column { name: "ps_partkey", index: 0 }], 2), input_partitions=2 |
| | RepartitionExec: partitioning=Hash([Column { name: "ps_partkey", index: 0 }, Column { name: "__value", index: 1 }], 2), input_partitions=2 |
| | ProjectionExec: expr=[ps_partkey@0 as ps_partkey, MIN(partsupp.ps_supplycost)@1 as __value] |
| | AggregateExec: mode=FinalPartitioned, gby=[ps_partkey@0 as ps_partkey], aggr=[MIN(partsupp.ps_supplycost)] |
| | CoalesceBatchesExec: target_batch_size=8192 |
Expand Down
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