diff --git a/be/src/pipeline/exec/table_function_operator.cpp b/be/src/pipeline/exec/table_function_operator.cpp
index 8146dc2ea0..6d38726742 100644
--- a/be/src/pipeline/exec/table_function_operator.cpp
+++ b/be/src/pipeline/exec/table_function_operator.cpp
@@ -21,4 +21,15 @@ namespace doris::pipeline {
 
 OPERATOR_CODE_GENERATOR(TableFunctionOperator, StatefulOperator)
 
+Status TableFunctionOperator::prepare(doris::RuntimeState* state) {
+    // just for speed up, the way is dangerous
+    _child_block.reset(_node->get_child_block());
+    return StatefulOperator::prepare(state);
+}
+
+Status TableFunctionOperator::close(doris::RuntimeState* state) {
+    _child_block.release();
+    return StatefulOperator::close(state);
+}
+
 } // namespace doris::pipeline
diff --git a/be/src/pipeline/exec/table_function_operator.h b/be/src/pipeline/exec/table_function_operator.h
index f2c0437101..1d544e8834 100644
--- a/be/src/pipeline/exec/table_function_operator.h
+++ b/be/src/pipeline/exec/table_function_operator.h
@@ -32,5 +32,9 @@ public:
 class TableFunctionOperator final : public StatefulOperator<TableFunctionOperatorBuilder> {
 public:
     TableFunctionOperator(OperatorBuilderBase* operator_builder, ExecNode* node);
+
+    Status prepare(RuntimeState* state) override;
+
+    Status close(RuntimeState* state) override;
 };
 } // namespace doris::pipeline
diff --git a/be/src/pipeline/exec/union_source_operator.cpp b/be/src/pipeline/exec/union_source_operator.cpp
index 08855b849c..c4f3250175 100644
--- a/be/src/pipeline/exec/union_source_operator.cpp
+++ b/be/src/pipeline/exec/union_source_operator.cpp
@@ -61,6 +61,9 @@ Status UnionSourceOperator::get_block(RuntimeState* state, vectorized::Block* bl
         std::unique_ptr<vectorized::Block> output_block;
         int child_idx = 0;
         _data_queue->get_block_from_queue(&output_block, &child_idx);
+        if (!output_block) {
+            return Status::OK();
+        }
         block->swap(*output_block);
         output_block->clear_column_data(_node->row_desc().num_materialized_slots());
         _data_queue->push_free_block(std::move(output_block), child_idx);
diff --git a/be/src/vec/core/block.cpp b/be/src/vec/core/block.cpp
index fe81a98968..0f4fe065f2 100644
--- a/be/src/vec/core/block.cpp
+++ b/be/src/vec/core/block.cpp
@@ -604,7 +604,7 @@ void Block::clear_column_data(int column_size) noexcept {
         }
     }
     for (auto& d : data) {
-        DCHECK(d.column->use_count() == 1);
+        DCHECK_EQ(d.column->use_count(), 1);
         (*std::move(d.column)).assume_mutable()->clear();
     }
 }
diff --git a/be/src/vec/exec/join/vhash_join_node.cpp b/be/src/vec/exec/join/vhash_join_node.cpp
index 586ee87c6f..34f40bf9b4 100644
--- a/be/src/vec/exec/join/vhash_join_node.cpp
+++ b/be/src/vec/exec/join/vhash_join_node.cpp
@@ -758,7 +758,10 @@ Status HashJoinNode::sink(doris::RuntimeState* state, vectorized::Block* in_bloc
     }
 
     if (_should_build_hash_table && eos) {
-        child(1)->close(state);
+        // For pipeline engine, children should be closed once this pipeline task is finished.
+        if (!state->enable_pipeline_exec()) {
+            child(1)->close(state);
+        }
         if (!_build_side_mutable_block.empty()) {
             if (_build_blocks->size() == _MAX_BUILD_BLOCK_COUNT) {
                 return Status::NotSupported(
@@ -802,7 +805,9 @@ Status HashJoinNode::sink(doris::RuntimeState* state, vectorized::Block* in_bloc
             _shared_hashtable_controller->signal(id());
         }
     } else if (!_should_build_hash_table) {
-        child(1)->close(state);
+        if (!state->enable_pipeline_exec()) {
+            child(1)->close(state);
+        }
         DCHECK(_shared_hashtable_controller != nullptr);
         DCHECK(_shared_hash_table_context != nullptr);
         auto wait_timer = ADD_TIMER(_build_phase_profile, "WaitForSharedHashTableTime");
diff --git a/be/src/vec/exec/vrepeat_node.cpp b/be/src/vec/exec/vrepeat_node.cpp
index 9c77b21bfc..04b1c91dcf 100644
--- a/be/src/vec/exec/vrepeat_node.cpp
+++ b/be/src/vec/exec/vrepeat_node.cpp
@@ -181,8 +181,9 @@ Status VRepeatNode::pull(doris::RuntimeState* state, vectorized::Block* output_b
     }
     DCHECK(output_block->rows() == 0);
 
-    DCHECK(_intermediate_block);
-    DCHECK_NE(_intermediate_block->rows(), 0);
+    if (!_intermediate_block || _intermediate_block->rows() == 0) {
+        return Status::OK();
+    }
 
     RETURN_IF_ERROR(get_repeated_block(_intermediate_block.get(), _repeat_id_idx, output_block));
 
@@ -201,6 +202,9 @@ Status VRepeatNode::pull(doris::RuntimeState* state, vectorized::Block* output_b
 }
 
 Status VRepeatNode::push(RuntimeState* state, vectorized::Block* input_block, bool eos) {
+    if (input_block->rows() == 0) {
+        return Status::OK();
+    }
     DCHECK(!_intermediate_block || _intermediate_block->rows() == 0);
     DCHECK(!_expr_ctxs.empty());
     _intermediate_block.reset(new Block());
diff --git a/be/src/vec/exec/vtable_function_node.cpp b/be/src/vec/exec/vtable_function_node.cpp
index b283988589..3e6b89fe60 100644
--- a/be/src/vec/exec/vtable_function_node.cpp
+++ b/be/src/vec/exec/vtable_function_node.cpp
@@ -70,7 +70,6 @@ Status VTableFunctionNode::prepare(RuntimeState* state) {
         }
     }
 
-    _child_block.reset(new Block());
     _cur_child_offset = -1;
 
     return Status::OK();
@@ -85,17 +84,17 @@ Status VTableFunctionNode::get_next(RuntimeState* state, Block* block, bool* eos
 
     // if child_block is empty, get data from child.
     if (need_more_input_data()) {
-        while (_child_block->rows() == 0 && !_child_eos) {
+        while (_child_block.rows() == 0 && !_child_eos) {
             RETURN_IF_ERROR_AND_CHECK_SPAN(
-                    child(0)->get_next_after_projects(state, _child_block.get(), &_child_eos),
+                    child(0)->get_next_after_projects(state, &_child_block, &_child_eos),
                     child(0)->get_next_span(), _child_eos);
         }
-        if (_child_eos && _child_block->rows() == 0) {
+        if (_child_eos && _child_block.rows() == 0) {
             *eos = true;
             return Status::OK();
         }
 
-        push(state, _child_block.get(), *eos);
+        push(state, &_child_block, *eos);
     }
 
     pull(state, block, eos);
@@ -104,8 +103,6 @@ Status VTableFunctionNode::get_next(RuntimeState* state, Block* block, bool* eos
 }
 
 Status VTableFunctionNode::get_expanded_block(RuntimeState* state, Block* output_block, bool* eos) {
-    DCHECK(_child_block != nullptr);
-
     size_t column_size = _output_slots.size();
     bool mem_reuse = output_block->mem_reuse();
 
@@ -122,7 +119,7 @@ Status VTableFunctionNode::get_expanded_block(RuntimeState* state, Block* output
         RETURN_IF_CANCELLED(state);
         RETURN_IF_ERROR(state->check_query_state("VTableFunctionNode, while getting next batch."));
 
-        if (_child_block->rows() == 0) {
+        if (_child_block.rows() == 0) {
             break;
         }
 
@@ -164,7 +161,7 @@ Status VTableFunctionNode::get_expanded_block(RuntimeState* state, Block* output
                     columns[i]->insert_default();
                     continue;
                 }
-                auto src_column = _child_block->get_by_position(i).column;
+                auto src_column = _child_block.get_by_position(i).column;
                 columns[i]->insert_from(*src_column, _cur_child_offset);
             }
 
@@ -207,13 +204,13 @@ Status VTableFunctionNode::get_expanded_block(RuntimeState* state, Block* output
 Status VTableFunctionNode::_process_next_child_row() {
     _cur_child_offset++;
 
-    if (_cur_child_offset >= _child_block->rows()) {
+    if (_cur_child_offset >= _child_block.rows()) {
         // release block use count.
         for (TableFunction* fn : _fns) {
             RETURN_IF_ERROR(fn->process_close());
         }
 
-        release_block_memory(*_child_block);
+        release_block_memory(_child_block);
         _cur_child_offset = -1;
         return Status::OK();
     }
diff --git a/be/src/vec/exec/vtable_function_node.h b/be/src/vec/exec/vtable_function_node.h
index 0f44e78e5c..451a35c739 100644
--- a/be/src/vec/exec/vtable_function_node.h
+++ b/be/src/vec/exec/vtable_function_node.h
@@ -31,18 +31,15 @@ public:
     Status prepare(RuntimeState* state) override;
     Status get_next(RuntimeState* state, Block* block, bool* eos) override;
 
-    bool need_more_input_data() { return !_child_block || !_child_block->rows(); }
+    bool need_more_input_data() { return !_child_block.rows(); }
 
     Status push(RuntimeState*, vectorized::Block* input_block, bool eos) override {
-        if (eos) {
+        if (input_block->rows() == 0) {
             return Status::OK();
         }
 
-        if (input_block != _child_block.get()) {
-            _child_block.reset(input_block);
-        }
         for (TableFunction* fn : _fns) {
-            RETURN_IF_ERROR(fn->process_init(_child_block.get()));
+            RETURN_IF_ERROR(fn->process_init(input_block));
         }
         RETURN_IF_ERROR(_process_next_child_row());
         return Status::OK();
@@ -54,6 +51,8 @@ public:
         return Status::OK();
     }
 
+    Block* get_child_block() { return &_child_block; }
+
 private:
     Status _process_next_child_row() override;
 
@@ -81,7 +80,7 @@ private:
 
     Status get_expanded_block(RuntimeState* state, Block* output_block, bool* eos);
 
-    std::unique_ptr<Block> _child_block;
+    Block _child_block;
     std::vector<SlotDescriptor*> _child_slots;
     std::vector<SlotDescriptor*> _output_slots;
 };
diff --git a/regression-test/conf/regression-conf.groovy b/regression-test/conf/regression-conf.groovy
index b9c8918be0..209dd24a3c 100644
--- a/regression-test/conf/regression-conf.groovy
+++ b/regression-test/conf/regression-conf.groovy
@@ -117,6 +117,6 @@ extEsPort = 9200
 extEsUser = "*******"
 extEsPassword = "***********"
 
-s3Endpoint = "cos.ap-hongkong.myqcloud.com/regression"
+s3Endpoint = "cos.ap-hongkong.myqcloud.com"
 s3BucketName = "doris-build-hk-1308700295"
 s3Region = "ap-hongkong"
\ No newline at end of file
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q01.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q01.out
new file mode 100644
index 0000000000..217ac6ead1
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q01.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q01 --
+AAAAAAAAAAABBAAA
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+AAAAAAAAACBPAAAA
+AAAAAAAAACCHAAAA
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q02.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q02.out
new file mode 100644
index 0000000000..892b5d996e
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q02.out
@@ -0,0 +1,2516 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q02 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q03.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q03.out
new file mode 100644
index 0000000000..69671c6a98
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q03.out
@@ -0,0 +1,92 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q03 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q04.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q04.out
new file mode 100644
index 0000000000..c31944aa5e
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q04.out
@@ -0,0 +1,11 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q04 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q05.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q05.out
new file mode 100644
index 0000000000..f6dda04eec
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q05.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q05 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q06.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q06.out
new file mode 100644
index 0000000000..54ad91363c
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q06.out
@@ -0,0 +1,48 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q06 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q07.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q07.out
new file mode 100644
index 0000000000..7803ee0bbe
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q07.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q07 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q08.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q08.out
new file mode 100644
index 0000000000..e97213795a
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q08.out
@@ -0,0 +1,8 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q08 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q09.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q09.out
new file mode 100644
index 0000000000..c786eef8d4
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q09.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q09 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q11.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q11.out
new file mode 100644
index 0000000000..1a969eb886
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q11.out
@@ -0,0 +1,90 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q11 --
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+AAAAAAAAPDFBAAAA	Terrance	Banks	Y	SLOVENIA	
+AAAAAAAAPEHEBAAA	Edith	Molina	Y	GREENLAND	
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q12.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q12.out
new file mode 100644
index 0000000000..480c5eddf0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q12.out
@@ -0,0 +1,103 @@
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+-- !pipeline_q12 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q13.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q13.out
new file mode 100644
index 0000000000..b346160902
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q13.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q13 --
+31.0	2551.86	2365.94	14195.66
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.out
new file mode 100644
index 0000000000..358c5bb00d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q14_1 --
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new file mode 100644
index 0000000000..6cc745243b
--- /dev/null
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+-- !pipeline_q14_2 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q15.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q15.out
new file mode 100644
index 0000000000..d482ecf405
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q15.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q15 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q16.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q16.out
new file mode 100644
index 0000000000..94a6ca2030
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q16.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q16 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q17.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q17.out
new file mode 100644
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--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q17.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q17 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q18.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q18.out
new file mode 100644
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--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q18.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q18 --
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+AAAAAAAAAMNDAAAA	\N	\N	\N	70.00	101.27	4284.88	65.82	-4341.58	1932.00	5.00
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+AAAAAAAAAOMCAAAA	\N	\N	\N	8.00	112.64	0.00	36.04	-509.20	1973.00	4.00
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q19.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q19.out
new file mode 100644
index 0000000000..df318c4b28
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q19.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q19 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q20.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q20.out
new file mode 100644
index 0000000000..0e20ecb439
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q20.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q20 --
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+AAAAAAAAMIGCAAAA	Carefully physical hotels must put together; similar details cannot appreciate by a standards. Rates can break m	Books	business	6.63	562.96	0.35
+AAAAAAAAMIMCAAAA	About likely houses like international members. Final, relevant birds answer after the paintings. Hungry, personal days borrow tiny, primary resources. As social relations could choose quite also 	Books	business	0.77	2889.22	1.84
+AAAAAAAAMKHAAAAA	Unions shall see enough over true attitudes; of course full variable	Books	business	8.90	15263.90	9.72
+AAAAAAAAMKNDAAAA	Special, clear elements would buy at a games. Things should spot today strange, only devices. Armies should like at a patients. Hands could perform simply narrow values. N	Books	business	1.28	1069.76	0.68
+AAAAAAAANACBAAAA	New teachers might demand never assets. Deeply bright ministers make generally never prime imports. Odd writings step common readers; talks take young, r	Books	business	2.95	3975.16	2.53
+AAAAAAAAPDNAAAAA	Local, unlikely bits sign completely. Areas feel only manufacturing legs. Amounts must go personal, very things; areas could take clo	Books	business	5.20	3545.37	2.25
+AAAAAAAAPEKCAAAA	Alone countries must use so old, international functions. Only public cases see in a words. Normal methods forget even communist changes; technical numbers convert either natu	Books	business	4.67	3899.62	2.48
+AAAAAAAAPGDBAAAA	Certainly remaining flowers can wonder then just significant papers; places secure below as a bombs. Other, domestic members must allow very polite thi	Books	business	0.60	12462.77	7.94
+AAAAAAAAPHJAAAAA	Possibly great customs suit close looks. Capable, frequent processes shall pass possible dangers; hard, private words act measures. Mysterious, acceptable fac	Books	business	6.64	6141.24	3.91
+AAAAAAAAAALDAAAA	Forward liable funds may not end from time to time local, domestic chiefs. Major, well-known newspapers can regain together new, white conclusions. Very vital employees can draw	Books	computers	17.54	588.01	0.31
+AAAAAAAAAHKDAAAA	Decisions play actually exclusive activities. Well assistant e	Books	computers	8.77	1619.66	0.85
+AAAAAAAAAKGDAAAA	Tonnes could use slowly off a servants. Initial letters must walk now companies; rapid, previous towns put here large, prime needs. Historical, negative grou	Books	computers	0.19	3319.10	1.75
+AAAAAAAAAOBCAAAA	Years should try in line with a conditions. Pp. spend well evenings. Other, afraid sides speculate at a years. Options ought to know leading, app	Books	computers	5.23	8468.08	4.47
+AAAAAAAABHEEAAAA	Subjects may remain officials. Forward, straight objects used to see wh	Books	computers	6.97	13658.40	7.22
+AAAAAAAABLMBAAAA	External improvements effect so tough words. Great roads cause quickly popular, black stories. Clearly white members might ask enough details. Min	Books	computers	31.74	4154.04	2.19
+AAAAAAAACHOCAAAA	Final governm	Books	computers	6.22	5102.98	2.69
+AAAAAAAACOHDAAAA	Left, important sports shall get on an specialists. Overall, e	Books	computers	3.56	14321.37	7.57
+AAAAAAAAEANCAAAA	Ye	Books	computers	9.75	1367.76	0.72
+AAAAAAAAEAPAAAAA	Just distinct children think individuals; popular arguments develop here cautious methods; appropriate children might beat. Proper, empirical hundreds fall oth	Books	computers	4.01	328.50	0.17
+AAAAAAAAEDMAAAAA	Books understand. Principles produce just at a premises. Years 	Books	computers	44.48	188.86	0.09
+AAAAAAAAFEEAAAAA	Capital, united feelings paint only things. Greatly financial economies should not pay somewhere soviet necessary armies; educational concepts mus	Books	computers	3.83	812.19	0.42
+AAAAAAAAFLFEAAAA	Social weeks may hope. However parental objects shall get just potential logical stations. Agreements attend on a arms; circa real reforms may interpret dogs. T	Books	computers	2.06	449.61	0.23
+AAAAAAAAGENAAAAA	Genera	Books	computers	2.84	950.58	0.50
+AAAAAAAAGHCBAAAA	Hundreds would meet regardless german, foreign scien	Books	computers	9.77	1969.60	1.04
+AAAAAAAAGNGBAAAA	Brilliant, massive prisons take still national others. Only northern guidelines go right by the lips. General, spiritual walls shall reach in a languages. British nations eat substantial polici	Books	computers	3.42	377.26	0.19
+AAAAAAAAHPADAAAA	Used, young sizes take requirements. Electoral, standard stones worry still private scenes. Major, still bedrooms say all once effective years. Long new moments will own after the	Books	computers	9.19	690.90	0.36
+AAAAAAAAIAMAAAAA	Alone walls mus	Books	computers	2.00	4530.16	2.39
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q21.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q21.out
new file mode 100644
index 0000000000..b823257a08
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q21.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q21 --
+	AAAAAAAAAABDAAAA	2565	1922
+	AAAAAAAAAAHDAAAA	2956	2052
+	AAAAAAAAAALAAAAA	2042	1793
+	AAAAAAAAABDAAAAA	3340	2626
+	AAAAAAAAACGCAAAA	2373	1771
+	AAAAAAAAACKCAAAA	2321	1856
+	AAAAAAAAACOBAAAA	1504	1397
+	AAAAAAAAADKBAAAA	1820	2163
+	AAAAAAAAAEADAAAA	2631	1965
+	AAAAAAAAAEOCAAAA	1659	1798
+	AAAAAAAAAFACAAAA	1965	1705
+	AAAAAAAAAFADAAAA	1769	1313
+	AAAAAAAAAHDEAAAA	2700	1985
+	AAAAAAAAAHHAAAAA	1578	1082
+	AAAAAAAAAIECAAAA	1756	1804
+	AAAAAAAAAIMCAAAA	3603	2951
+	AAAAAAAAAJACAAAA	2109	1989
+	AAAAAAAAAJKBAAAA	2573	3540
+	AAAAAAAAALBEAAAA	3458	2992
+	AAAAAAAAALCEAAAA	1720	1810
+	AAAAAAAAALECAAAA	2569	1946
+	AAAAAAAAALNBAAAA	2552	1750
+	AAAAAAAAANFEAAAA	2022	2269
+	AAAAAAAAAOBBAAAA	1603	1592
+	AAAAAAAAAOIBAAAA	2982	2540
+	AAAAAAAAAPJBAAAA	2344	2593
+	AAAAAAAABAPDAAAA	2182	2787
+	AAAAAAAABDCEAAAA	2844	2069
+	AAAAAAAABDDDAAAA	2417	2537
+	AAAAAAAABDJAAAAA	1584	1666
+	AAAAAAAABEODAAAA	2141	2649
+	AAAAAAAABFCCAAAA	2745	2020
+	AAAAAAAABFMBAAAA	1642	1364
+	AAAAAAAABHPCAAAA	1923	1780
+	AAAAAAAABIDBAAAA	1956	2836
+	AAAAAAAABIGBAAAA	2023	2344
+	AAAAAAAABIJBAAAA	1977	2728
+	AAAAAAAABJFEAAAA	1891	2390
+	AAAAAAAABLDEAAAA	1983	1797
+	AAAAAAAABNIDAAAA	2485	2324
+	AAAAAAAABNLDAAAA	2385	2786
+	AAAAAAAABOMBAAAA	2291	2092
+	AAAAAAAACAAAAAAA	2233	2560
+	AAAAAAAACBCDAAAA	1540	2012
+	AAAAAAAACBIAAAAA	2394	2122
+	AAAAAAAACBPBAAAA	1790	1661
+	AAAAAAAACCMDAAAA	2654	2691
+	AAAAAAAACDBCAAAA	1804	2072
+	AAAAAAAACFEAAAAA	1941	1567
+	AAAAAAAACFHDAAAA	1696	1147
+	AAAAAAAACGFDAAAA	2123	2265
+	AAAAAAAACHPCAAAA	2933	2174
+	AAAAAAAACIGDAAAA	2618	2399
+	AAAAAAAACJCBAAAA	2728	2367
+	AAAAAAAACJLAAAAA	1350	1732
+	AAAAAAAACLAEAAAA	2578	2329
+	AAAAAAAACLGAAAAA	1842	1588
+	AAAAAAAACLLBAAAA	3418	2657
+	AAAAAAAACLOBAAAA	3115	2560
+	AAAAAAAACMADAAAA	1991	2243
+	AAAAAAAACMJAAAAA	1261	1855
+	AAAAAAAACMLAAAAA	3288	2753
+	AAAAAAAACMPDAAAA	1320	1676
+	AAAAAAAACNGBAAAA	2340	2118
+	AAAAAAAACNHDAAAA	3519	3348
+	AAAAAAAACNPCAAAA	2561	1948
+	AAAAAAAADCPCAAAA	2664	2627
+	AAAAAAAADDHAAAAA	1313	1926
+	AAAAAAAADDNDAAAA	1109	835
+	AAAAAAAADEAAAAAA	2141	1847
+	AAAAAAAADEJAAAAA	3142	2723
+	AAAAAAAADFKBAAAA	1470	1650
+	AAAAAAAADGCCAAAA	2113	2331
+	AAAAAAAADGFCAAAA	2201	2928
+	AAAAAAAADHPAAAAA	2467	2133
+	AAAAAAAADMBAAAAA	3085	2087
+	AAAAAAAADPABAAAA	3494	3081
+	AAAAAAAAEAECAAAA	2133	2148
+	AAAAAAAAEAPAAAAA	1560	1275
+	AAAAAAAAECGCAAAA	2815	3307
+	AAAAAAAAEDPDAAAA	2731	1883
+	AAAAAAAAEEECAAAA	2024	1902
+	AAAAAAAAEEMCAAAA	2624	2387
+	AAAAAAAAEFFAAAAA	2047	1878
+	AAAAAAAAEGJAAAAA	2403	2633
+	AAAAAAAAEGMAAAAA	2784	2772
+	AAAAAAAAEGOCAAAA	2389	1753
+	AAAAAAAAEHFDAAAA	1940	1420
+	AAAAAAAAEHLBAAAA	2320	2057
+	AAAAAAAAEHPAAAAA	1898	1853
+	AAAAAAAAEIPBAAAA	2930	2326
+	AAAAAAAAEJAEAAAA	2582	1836
+	AAAAAAAAEJIBAAAA	2257	1681
+	AAAAAAAAEJJAAAAA	2791	1941
+	AAAAAAAAEJJDAAAA	3410	2405
+	AAAAAAAAEJNCAAAA	2472	2067
+	AAAAAAAAEJPDAAAA	1219	1229
+	AAAAAAAAEKEBAAAA	2047	1713
+	AAAAAAAAEMEAAAAA	2502	1897
+	AAAAAAAAEMKCAAAA	2362	2042
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q22.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q22.out
new file mode 100644
index 0000000000..3546e63990
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q22.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q22 --
+ationbarn station	\N	\N	\N	430.3577235772358
+ationbarn station	amalgbrand #8	\N	\N	430.3577235772358
+ationbarn station	amalgbrand #8	bathroom	\N	430.3577235772358
+ationbarn station	amalgbrand #8	bathroom	Home	430.3577235772358
+ationoughtn stn st	\N	\N	\N	435.26506024096386
+ationoughtn stn st	edu packimporto #2	\N	\N	435.26506024096386
+ationoughtn stn st	edu packimporto #2	sports-apparel	\N	435.26506024096386
+ationoughtn stn st	edu packimporto #2	sports-apparel	Men	435.26506024096386
+ationationprin st	\N	\N	\N	435.5102880658436
+ationationprin st	amalgexporti #2	\N	\N	435.5102880658436
+ationationprin st	amalgexporti #2	newborn	\N	435.5102880658436
+ationationprin st	amalgexporti #2	newborn	Children	435.5102880658436
+oughtcallyn stantiought	\N	\N	\N	436.49402390438246
+oughtcallyn stantiought	corpcorp #2	\N	\N	436.49402390438246
+oughtcallyn stantiought	corpcorp #2	rings	\N	436.49402390438246
+oughtcallyn stantiought	corpcorp #2	rings	Jewelry	436.49402390438246
+callyesebaration	\N	\N	\N	437.3943089430894
+callyesebaration	namelesscorp #7	\N	\N	437.3943089430894
+callyesebaration	namelesscorp #7	mens watch	\N	437.3943089430894
+callyesebaration	namelesscorp #7	mens watch	Jewelry	437.3943089430894
+n stprieingationought	\N	\N	\N	439.06477732793525
+n stprieingationought	namelesscorp #8	\N	\N	439.06477732793525
+n stprieingationought	namelesscorp #8	mens watch	\N	439.06477732793525
+n stprieingationought	namelesscorp #8	mens watch	Jewelry	439.06477732793525
+priationn stn st	\N	\N	\N	439.096
+priationn stn st	importoscholar #2	\N	\N	439.096
+priationn stn st	importoscholar #2	country	\N	439.096
+priationn stn st	importoscholar #2	country	Music	439.096
+n stcallyableoughtought	\N	\N	\N	439.71836734693875
+n stcallyableoughtought	amalgimporto #2	\N	\N	439.71836734693875
+n stcallyableoughtought	amalgimporto #2	accessories	\N	439.71836734693875
+n stcallyableoughtought	amalgimporto #2	accessories	Men	439.71836734693875
+n stbarought	\N	\N	\N	440.31474103585657
+n stbarought	corpbrand #10	\N	\N	440.31474103585657
+n stbarought	corpbrand #10	rugs	\N	440.31474103585657
+n stbarought	corpbrand #10	rugs	Home	440.31474103585657
+n stn stantioughtought	\N	\N	\N	440.92857142857144
+n stn stantioughtought	exportimaxi #8	\N	\N	440.92857142857144
+n stn stantioughtought	exportimaxi #8	computers	\N	440.92857142857144
+n stn stantioughtought	exportimaxi #8	computers	Books	440.92857142857144
+n stcallyesepriought	\N	\N	\N	441.54183266932273
+n stcallyesepriought	edu packscholar #2	\N	\N	441.54183266932273
+n stcallyesepriought	edu packscholar #2	classical	\N	441.54183266932273
+n stcallyesepriought	edu packscholar #2	classical	Music	441.54183266932273
+priantiationation	\N	\N	\N	441.6707317073171
+priantiationation	importoimporto #2	\N	\N	441.6707317073171
+priantiationation	importoimporto #2	shirts	\N	441.6707317073171
+priantiationation	importoimporto #2	shirts	Men	441.6707317073171
+oughtbarantiese	\N	\N	\N	441.7459677419355
+oughtbarantiese	exportiedu pack #2	\N	\N	441.7459677419355
+oughtbarantiese	exportiedu pack #2	kids	\N	441.7459677419355
+oughtbarantiese	exportiedu pack #2	kids	Shoes	441.7459677419355
+oughtbarprin st	\N	\N	\N	442.0734693877551
+oughtbarprin st	edu packscholar #2	\N	\N	442.0734693877551
+oughtbarprin st	edu packscholar #2	classical	\N	442.0734693877551
+oughtbarprin st	edu packscholar #2	classical	Music	442.0734693877551
+ationeingation	\N	\N	\N	443.78225806451616
+ationeingation	amalgexporti #2	\N	\N	443.78225806451616
+ationeingation	amalgexporti #2	newborn	\N	443.78225806451616
+ationeingation	amalgexporti #2	newborn	Children	443.78225806451616
+ationbarn stableought	\N	\N	\N	444.09274193548384
+ationbarn stableought	corpbrand #8	\N	\N	444.09274193548384
+ationbarn stableought	corpbrand #8	rugs	\N	444.09274193548384
+ationbarn stableought	corpbrand #8	rugs	Home	444.09274193548384
+eseationcallyableought	\N	\N	\N	444.5443548387097
+eseationcallyableought	importoexporti #1	\N	\N	444.5443548387097
+eseationcallyableought	importoexporti #1	infants	\N	444.5443548387097
+eseationcallyableought	importoexporti #1	infants	Children	444.5443548387097
+eingableableationought	\N	\N	\N	444.7142857142857
+eingableableationought	amalgimporto #1	\N	\N	444.7142857142857
+eingableableationought	amalgimporto #1	accessories	\N	444.7142857142857
+eingableableationought	amalgimporto #1	accessories	Men	444.7142857142857
+n steseantiation	\N	\N	\N	445.33196721311475
+n steseantiation	univmaxi #10	\N	\N	445.33196721311475
+n steseantiation	univmaxi #10	pools	\N	445.33196721311475
+n steseantiation	univmaxi #10	pools	Sports	445.33196721311475
+antibareingantiought	\N	\N	\N	445.4065040650407
+antibareingantiought	importoimporto #2	\N	\N	445.4065040650407
+antibareingantiought	importoimporto #2	shirts	\N	445.4065040650407
+antibareingantiought	importoimporto #2	shirts	Men	445.4065040650407
+ationantibarationought	\N	\N	\N	445.4190871369295
+ationantibarationought	univmaxi #8	\N	\N	445.4190871369295
+ationantibarationought	univmaxi #8	pools	\N	445.4190871369295
+ationantibarationought	univmaxi #8	pools	Sports	445.4190871369295
+oughtpricallybarought	\N	\N	\N	445.4859437751004
+oughtpricallybarought	edu packexporti #2	\N	\N	445.4859437751004
+oughtpricallybarought	edu packexporti #2	school-uniforms	\N	445.4859437751004
+oughtpricallybarought	edu packexporti #2	school-uniforms	Children	445.4859437751004
+ableationprieing	\N	\N	\N	445.99583333333334
+ableationprieing	amalgamalg #1	\N	\N	445.99583333333334
+ableationprieing	amalgamalg #1	dresses	\N	445.99583333333334
+ableationprieing	amalgamalg #1	dresses	Women	445.99583333333334
+pribarpribarought	\N	\N	\N	446.0408163265306
+pribarpribarought	edu packimporto #2	\N	\N	446.0408163265306
+pribarpribarought	edu packimporto #2	sports-apparel	\N	446.0408163265306
+pribarpribarought	edu packimporto #2	sports-apparel	Men	446.0408163265306
+oughtn stprin st	\N	\N	\N	447.26666666666665
+oughtn stprin st	exportiamalgamalg #8	\N	\N	447.26666666666665
+oughtn stprin st	exportiamalgamalg #8	stereo	\N	447.26666666666665
+oughtn stprin st	exportiamalgamalg #8	stereo	Electronics	447.26666666666665
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.out
new file mode 100644
index 0000000000..aa0ee6e1c3
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q23_1 --
+17030.91
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.out
new file mode 100644
index 0000000000..c23bf5e7c6
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.out
@@ -0,0 +1,7 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q23_2 --
+	Robert	598.86
+Brown	Monika	6031.52
+Collins	Gordon	727.57
+Green	Jesse	9672.96
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.out
new file mode 100644
index 0000000000..dcdea5db4a
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.out
@@ -0,0 +1,10 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q24_1 --
+	Tommy	able	38118.08
+Holt	Curtis	able	8225.80
+Kunz	Lee	able	34631.52
+Littlefield	Clarence	able	127380.00
+Pettit	Richard	able	3930.52
+Townsend	Franklin	able	68983.20
+Winchester	Margaret	bar	14269.20
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.out
new file mode 100644
index 0000000000..dd055b62fe
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q24_2 --
+Griffith	Ray	able	161564.48
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q25.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q25.out
new file mode 100644
index 0000000000..8970625e94
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q25.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q25 --
+AAAAAAAADPMBAAAA	Things know alone letters. Flights should tend even jewish fees. Civil plans could not cry also social days; other losses might not pay walls; still able signs should not remove too human 	AAAAAAAAHAAAAAAA	ation	12.84	91.41	-1329.46
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q26.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q26.out
new file mode 100644
index 0000000000..9ea9702eb0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q26.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q26 --
+AAAAAAAAAAAEAAAA	63.0	30.01	0.00	25.29
+AAAAAAAAAABEAAAA	96.0	75.34	0.00	10.54
+AAAAAAAAAACAAAAA	73.0	199.77	0.00	41.95
+AAAAAAAAAACCAAAA	23.5	26.62	110.97	14.20
+AAAAAAAAAADBAAAA	23.0	131.56	0.00	26.31
+AAAAAAAAAADEAAAA	15.0	283.91	0.00	19.87
+AAAAAAAAAAEAAAAA	71.0	176.71	0.00	142.69
+AAAAAAAAAAEBAAAA	96.0	66.74	1014.39	16.01
+AAAAAAAAAAFCAAAA	85.0	76.81	143.61	42.24
+AAAAAAAAAAGEAAAA	47.0	136.92	0.00	95.27
+AAAAAAAAAAHBAAAA	27.0	105.84	0.00	35.98
+AAAAAAAAAAIDAAAA	78.33333333333333	126.37	0.00	61.94
+AAAAAAAAAAJBAAAA	60.333333333333336	85.59	23.20	15.50
+AAAAAAAAAAKDAAAA	21.0	84.41	0.00	26.85
+AAAAAAAAAALCAAAA	20.0	62.79	0.00	48.97
+AAAAAAAAAALDAAAA	49.0	73.15	0.00	21.00
+AAAAAAAAAAMCAAAA	81.0	229.62	15221.17	202.06
+AAAAAAAAAANAAAAA	42.0	79.99	0.00	76.79
+AAAAAAAAAANDAAAA	94.0	27.92	0.00	1.67
+AAAAAAAAAAOAAAAA	66.0	44.29	25.82	10.05
+AAAAAAAAAAOCAAAA	34.333333333333336	84.49	1133.95	63.43
+AAAAAAAAABABAAAA	45.0	79.82	0.00	67.84
+AAAAAAAAABAEAAAA	1.0	171.54	0.00	49.74
+AAAAAAAAABBAAAAA	8.0	279.08	0.00	55.81
+AAAAAAAAABBCAAAA	56.0	45.97	1286.38	23.44
+AAAAAAAAABBDAAAA	47.0	22.44	0.00	21.99
+AAAAAAAAABCBAAAA	84.0	43.87	0.00	28.51
+AAAAAAAAABEDAAAA	43.0	105.77	668.40	37.01
+AAAAAAAAABFEAAAA	66.0	239.55	0.00	184.45
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q27.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q27.out
new file mode 100644
index 0000000000..b3510ee48b
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q27.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q27 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q28.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q28.out
new file mode 100644
index 0000000000..b7530ee4cb
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q28.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q28 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q29.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q29.out
new file mode 100644
index 0000000000..4eee06efbd
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q29.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q29 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q30.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q30.out
new file mode 100644
index 0000000000..e6b9b2cc8b
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q30.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q30 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q31.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q31.out
new file mode 100644
index 0000000000..59b85c0d1d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q31.out
@@ -0,0 +1,53 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q31 --
+Atchison County	2000	0.80	0.24	11.94	3.16
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+Bourbon County	2000	1.91	0.98	3.36	1.38
+Boyd County	2000	1.08	0.81	1.16	0.74
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+Heard County	2000	4.10	1.26	3.50	1.12
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+Ingham County	2000	0.57	0.38	1.30	0.99
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+Marion County	2000	1.15	0.91	2.44	1.85
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+Williamson County	2000	2.98	0.39	5.80	4.39
+Wright County	2000	5.02	1.97	4.07	1.96
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q32.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q32.out
new file mode 100644
index 0000000000..4b92b8e7bd
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q32.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q32 --
+28038.14
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q33.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q33.out
new file mode 100644
index 0000000000..e74f4c2f3d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q33.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q33 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q34.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q34.out
new file mode 100644
index 0000000000..b9ea160d9d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q34.out
@@ -0,0 +1,454 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q34 --
+			Y	16278	15
+			Y	146557	16
+			N	55640	15
+			N	58614	15
+			N	96514	15
+			N	220211	15
+				148695	16
+				151297	15
+		Mr.	Y	214003	15
+		Ms.		54536	16
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+	Joshua	Sir	N	108357	15
+	Judy	Dr.		234350	16
+	Roy			200033	15
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q36.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q36.out
new file mode 100644
index 0000000000..245986a4a0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q36.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q36 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q37.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q37.out
new file mode 100644
index 0000000000..f97ceb927c
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q37.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q37 --
+AAAAAAAAHFMBAAAA	Jobs crack at all public countries. Possible, traditional initiatives will make british problems. Fiscal years call extremely just growing pupils. Soon dutch voices	86.11
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q38.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q38.out
new file mode 100644
index 0000000000..d23635acc0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q38.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q38 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.out
new file mode 100644
index 0000000000..f80ff8a681
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.out
@@ -0,0 +1,246 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q39_1 --
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+5	17943	1	209.75	1.3717071339917377	5	17943	2	428.0	1.1090799349168265
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.out
new file mode 100644
index 0000000000..73ac2e81f0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.out
@@ -0,0 +1,17 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q39_2 --
+1	1569	1	212.0	1.630213519639535	1	1569	2	239.25	1.2641513267800557
+1	5627	1	282.75	1.5657032366359889	1	5627	2	297.5	1.2084286841430678
+1	7999	1	166.25	1.7924231710846223	1	7999	2	375.3333333333333	1.008092263550718
+1	8611	1	300.5	1.519154518414795	1	8611	2	243.75	1.2342122780960432
+1	15345	1	148.5	1.5295784035794022	1	15345	2	246.5	1.5087987747231526
+2	71	1	221.5	1.563974108334745	2	71	2	309.0	1.4917057895885681
+2	6103	1	194.33333333333334	1.5160670179307387	2	6103	2	158.5	1.2743698636165062
+2	6489	1	268.0	1.6956372368432266	2	6489	2	389.0	1.4105780519299767
+2	15839	1	353.0	1.5063684437542906	2	15839	2	255.5	1.2362393182894105
+3	7207	1	329.6666666666667	1.5954482160720398	3	7207	2	414.5	1.017919707908937
+3	10547	1	182.33333333333334	1.5325641514869042	3	10547	2	320.25	1.302441844373152
+3	12867	1	278.25	1.640380012394735	3	12867	2	350.75	1.2006933321742796
+4	947	1	247.5	1.6933181813486973	4	947	2	203.33333333333334	1.205433145161931
+5	3137	1	271.25	1.575453220592864	5	3137	2	380.0	1.0834203388600319
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q40.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q40.out
new file mode 100644
index 0000000000..6252b83312
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q40.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q40 --
+TN	AAAAAAAAAABDAAAA	0.00	-82.10
+TN	AAAAAAAAAACDAAAA	-216.54	158.04
+TN	AAAAAAAAAAHDAAAA	186.55	0.00
+TN	AAAAAAAAAALAAAAA	0.00	48.23
+TN	AAAAAAAAABBDAAAA	98.61	332.71
+TN	AAAAAAAAABDAAAAA	0.00	213.64
+TN	AAAAAAAAACGCAAAA	63.68	0.00
+TN	AAAAAAAAACHCAAAA	102.68	51.89
+TN	AAAAAAAAACKCAAAA	128.93	44.82
+TN	AAAAAAAAACLDAAAA	205.44	-948.62
+TN	AAAAAAAAACOBAAAA	207.32	24.89
+TN	AAAAAAAAACPDAAAA	87.75	53.99
+TN	AAAAAAAAADGBAAAA	44.31	222.48
+TN	AAAAAAAAADKBAAAA	0.00	-471.87
+TN	AAAAAAAAAEADAAAA	58.24	0.00
+TN	AAAAAAAAAEOCAAAA	19.91	214.70
+TN	AAAAAAAAAFACAAAA	271.82	163.17
+TN	AAAAAAAAAFADAAAA	2.35	28.32
+TN	AAAAAAAAAFDCAAAA	-378.05	-303.27
+TN	AAAAAAAAAGIDAAAA	307.61	-19.29
+TN	AAAAAAAAAHDEAAAA	80.58	-476.72
+TN	AAAAAAAAAHHAAAAA	8.27	155.10
+TN	AAAAAAAAAHJBAAAA	39.24	0.00
+TN	AAAAAAAAAIECAAAA	82.40	3.91
+TN	AAAAAAAAAIEEAAAA	20.40	-151.09
+TN	AAAAAAAAAIMCAAAA	24.47	-150.30
+TN	AAAAAAAAAJACAAAA	49.09	82.10
+TN	AAAAAAAAAJCAAAAA	121.18	63.78
+TN	AAAAAAAAAJKBAAAA	27.94	8.97
+TN	AAAAAAAAALBEAAAA	88.26	30.23
+TN	AAAAAAAAALCEAAAA	93.52	92.02
+TN	AAAAAAAAALECAAAA	64.20	15.16
+TN	AAAAAAAAALNBAAAA	4.20	148.27
+TN	AAAAAAAAAMBEAAAA	28.44	0.00
+TN	AAAAAAAAAMPBAAAA	0.00	131.93
+TN	AAAAAAAAANFEAAAA	0.00	-137.34
+TN	AAAAAAAAAOBBAAAA	0.00	55.62
+TN	AAAAAAAAAOIBAAAA	150.41	254.28
+TN	AAAAAAAAAPBAAAAA	70.40	0.00
+TN	AAAAAAAAAPJBAAAA	45.27	334.40
+TN	AAAAAAAAAPLAAAAA	50.20	29.15
+TN	AAAAAAAAAPLDAAAA	0.00	32.39
+TN	AAAAAAAABAPDAAAA	93.42	145.87
+TN	AAAAAAAABBIDAAAA	296.77	30.96
+TN	AAAAAAAABDCEAAAA	-1771.08	-54.78
+TN	AAAAAAAABDDDAAAA	111.12	280.59
+TN	AAAAAAAABDJAAAAA	0.00	79.55
+TN	AAAAAAAABEFDAAAA	0.00	3.43
+TN	AAAAAAAABEODAAAA	269.90	297.58
+TN	AAAAAAAABFMBAAAA	110.83	-941.40
+TN	AAAAAAAABFNAAAAA	47.86	0.00
+TN	AAAAAAAABFOCAAAA	46.34	83.52
+TN	AAAAAAAABHPCAAAA	27.37	77.62
+TN	AAAAAAAABIDBAAAA	196.62	5.57
+TN	AAAAAAAABIGBAAAA	425.34	0.00
+TN	AAAAAAAABIJBAAAA	209.63	0.00
+TN	AAAAAAAABJFEAAAA	7.33	55.16
+TN	AAAAAAAABKFAAAAA	0.00	138.14
+TN	AAAAAAAABKMCAAAA	27.17	54.97
+TN	AAAAAAAABLDEAAAA	170.29	0.00
+TN	AAAAAAAABNHBAAAA	58.06	-337.89
+TN	AAAAAAAABNIDAAAA	54.40	35.02
+TN	AAAAAAAABNLAAAAA	0.00	168.38
+TN	AAAAAAAABNLDAAAA	0.00	96.41
+TN	AAAAAAAABNMCAAAA	202.41	49.53
+TN	AAAAAAAABOCCAAAA	4.73	69.84
+TN	AAAAAAAABOMBAAAA	63.67	163.49
+TN	AAAAAAAACAAAAAAA	121.91	0.00
+TN	AAAAAAAACAADAAAA	-1107.61	0.00
+TN	AAAAAAAACAJCAAAA	115.81	173.05
+TN	AAAAAAAACBCDAAAA	18.94	226.38
+TN	AAAAAAAACBFAAAAA	0.00	97.41
+TN	AAAAAAAACBIAAAAA	2.14	84.66
+TN	AAAAAAAACBPBAAAA	95.44	26.68
+TN	AAAAAAAACCABAAAA	160.43	135.86
+TN	AAAAAAAACCHDAAAA	0.00	121.62
+TN	AAAAAAAACCMDAAAA	-115.87	124.38
+TN	AAAAAAAACDBCAAAA	16.62	3.40
+TN	AAAAAAAACDECAAAA	-3114.60	0.00
+TN	AAAAAAAACEEAAAAA	34.68	26.41
+TN	AAAAAAAACELAAAAA	130.59	154.63
+TN	AAAAAAAACELDAAAA	0.00	181.07
+TN	AAAAAAAACFEAAAAA	3.78	-315.13
+TN	AAAAAAAACFHDAAAA	0.00	1.80
+TN	AAAAAAAACGFDAAAA	-386.87	96.92
+TN	AAAAAAAACHHDAAAA	143.17	251.64
+TN	AAAAAAAACHPCAAAA	0.17	198.29
+TN	AAAAAAAACJCBAAAA	-918.65	270.96
+TN	AAAAAAAACJDCAAAA	0.00	130.15
+TN	AAAAAAAACJLAAAAA	63.96	91.27
+TN	AAAAAAAACKFCAAAA	-540.59	35.64
+TN	AAAAAAAACKHAAAAA	204.52	110.61
+TN	AAAAAAAACKIAAAAA	18.43	-63.65
+TN	AAAAAAAACLAEAAAA	116.07	0.00
+TN	AAAAAAAACLGAAAAA	108.10	111.14
+TN	AAAAAAAACLKAAAAA	143.05	19.59
+TN	AAAAAAAACLLBAAAA	0.00	178.10
+TN	AAAAAAAACLOBAAAA	-2200.72	14.13
+TN	AAAAAAAACMADAAAA	71.42	-13.64
+TN	AAAAAAAACMJAAAAA	0.00	358.31
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q41.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q41.out
new file mode 100644
index 0000000000..5ac1a90b35
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q41.out
@@ -0,0 +1,7 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q41 --
+ableationableought
+anticallyeingese
+callycallyeingese
+oughtationableought
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q42.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q42.out
new file mode 100644
index 0000000000..83c07f2120
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q42.out
@@ -0,0 +1,13 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q42 --
+2000	7	Home	458017.85
+2000	3	Children	370261.29
+2000	2	Men	368718.95
+2000	1	Women	320132.43
+2000	10	Electronics	281421.74
+2000	5	Music	223420.70
+2000	4	Shoes	221242.25
+2000	8	Sports	200806.45
+2000	6	Jewelry	167920.91
+2000	9	Books	161721.11
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q43.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q43.out
new file mode 100644
index 0000000000..bb90ac804e
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q43.out
@@ -0,0 +1,9 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q43 --
+able	AAAAAAAACAAAAAAA	517884.59	469230.50	505832.67	443696.30	479716.97	462447.50	503064.60
+ation	AAAAAAAAHAAAAAAA	508811.68	474290.02	448808.84	492870.99	498127.64	474355.89	505906.68
+bar	AAAAAAAAKAAAAAAA	496021.80	459933.01	479825.96	474630.24	482326.79	478330.87	505252.22
+eing	AAAAAAAAIAAAAAAA	498752.97	476119.01	485965.24	454921.28	491953.89	476014.69	484633.67
+ese	AAAAAAAAEAAAAAAA	493724.01	499637.85	452314.62	466232.23	481922.38	477933.29	500577.95
+ought	AAAAAAAABAAAAAAA	505735.34	471490.23	463248.39	482690.52	485818.98	481816.20	491354.68
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q44.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q44.out
new file mode 100644
index 0000000000..0068bb1c62
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q44.out
@@ -0,0 +1,13 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q44 --
+1	oughtantiprin st	callyeingbarcallyought
+2	barcallyprioughtought	bareseationcallyought
+3	ableeingantiable	barn stcallycally
+4	n stesebarn st	eingoughtn stn st
+5	antioughtationbarought	callycallybarantiought
+6	callyeseationantiought	
+7	priableeseableought	eseableablepriought
+8	ableoughtableeseought	ationoughtantianti
+9	esebarableeseought	callyn stantieseought
+10	eingoughtn station	barcallyableought
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q46.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q46.out
new file mode 100644
index 0000000000..aa862fb619
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q46.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q46 --
+			Bunker Hill	133136	4983.83	-11549.37
+			Plainview	1459	0.00	-3836.32
+			Red Hill	93581	258.51	-4603.87
+			Red Hill	126394	0.00	-7598.00
+		Antioch	Belmont	149232	1500.41	-3669.87
+		Ashland	Philadelphia	155902	2303.72	-9449.87
+		Bayview	Springdale	162862	529.16	-18595.60
+		Belleview	Nichols	59976	13402.13	-15556.69
+		Blair	Hamilton	206536	0.00	-1287.17
+		Brownsville	Bunker Hill	159429	4557.21	-21733.27
+		Brownsville	Mechanicsburg	60829	2340.86	-7083.55
+		Brunswick	Mountain View	123733	2956.13	-17208.64
+		Buckingham	Siloam	93903	319.04	-8652.20
+		Bunker Hill	Antioch	199878	0.00	-773.27
+		Bunker Hill	Greenfield	127182	862.70	-15471.32
+		Bunker Hill	Hopewell	53497	1896.43	-14367.16
+		Cedar Grove	Jackson	81259	8.55	-11335.94
+		Centerville	Bethel	210801	6002.21	-18193.72
+		Centerville	Mount Olive	66053	2445.21	-8995.89
+		Centerville	Mount Zion	83650	3896.13	-11148.64
+		Centerville	Providence	62604	156.21	-13626.64
+		Clifford		129227	3169.89	-16776.31
+		Clifton	Mount Vernon	95501	3283.17	-13463.75
+		Concord	Stringtown	97052	2949.46	-762.97
+		Cordova	Crossroads	85385	38.97	-1522.58
+		Crossroads	Buena Vista	221999	6605.50	-14108.43
+		Crossroads	Unionville	58203	1393.55	-11986.88
+		Deerfield	Riverside	226924	1511.10	-12462.44
+		Derby	Riverdale	140237	4006.86	-7955.30
+		Edgewood	Hopewell	167421	3066.70	-20260.27
+		Empire	Five Forks	43396	1194.37	-7701.69
+		Empire	Midway	165554	258.33	-16775.39
+		Enterprise	Highland Park	147688	2213.54	-1680.47
+		Enterprise	Red Hill	115200	7016.43	-19331.97
+		Fairfield	Bridgeport	13112	1111.88	-10033.40
+		Fairfield	Midway	671	438.90	2207.18
+		Fairfield	Shiloh	149867	2723.49	-7041.50
+		Fairfield	Springdale	66752	3844.64	-8300.75
+		Fairview	Florence	90439	7932.81	-18236.19
+		Five Forks	Lakewood	186991	4960.22	-10929.93
+		Five Forks	Shady Grove	239479	374.43	-21828.49
+		Five Points	Union Hill	57715	3618.57	-14013.63
+		Florence	Edgewood	110370	434.48	-7367.45
+		Florence	Oak Ridge	45989	164.64	-13462.57
+		Florence	Spring Hill	104935	40.79	-9961.14
+		Forest Hills	Argyle	139387	508.08	-3990.97
+		Forest Hills	Ashland	214771	1585.36	-2846.81
+		Forest Hills	Riverside	184229	2485.60	-7673.40
+		Franklin	Floyd	230648	791.64	-8627.59
+		Friendship	Ashland	11043	382.33	-4808.33
+		Georgetown	Clifton	123750	189.60	-5249.53
+		Georgetown	Glendale	27617	787.61	-8592.30
+		Georgetown	Red Hill	234956	50.90	-12681.22
+		Georgetown	Summit	120620	1005.21	-3159.14
+		Gladstone	Hopewell	229820	184.68	-9366.12
+		Glendale	Four Points	167992	29.32	3058.64
+		Glendale	Indian Village	203729	3173.01	-5661.90
+		Glendale	Marion	51036	993.75	-10587.28
+		Glendale	Mount Pleasant	159389	2415.56	-12041.36
+		Glendale	West Liberty	5509	694.86	-23013.69
+		Glenwood	Antioch	86386	413.05	-6649.42
+		Glenwood	Clinton	4460	1568.11	-17245.05
+		Glenwood	Clinton	90728	3371.21	-15159.10
+		Granite	Clinton	211465	8156.94	-18944.88
+		Green Acres	Avery	191571	383.26	-7422.35
+		Greenfield	Edgewood	69782	1026.47	-14367.20
+		Greenfield	Red Oak	214549	1610.58	-1902.29
+		Greenfield	Riverdale	133538	9390.10	-15802.28
+		Greenville	Mountain View	103238	4318.13	-4564.09
+		Greenville	Shiloh	155915	0.00	-9362.72
+		Greenwood	Bridgeport	228626	994.35	-11510.47
+		Greenwood	Lakeside	37922	2446.00	-7423.85
+		Greenwood	Macedonia	133102	0.00	-2193.62
+		Hamilton	Cedar	112720	201.76	-11031.54
+		Hamilton	Liberty	121398	536.72	-8979.34
+		Hamilton	Valley View	161021	222.62	-14659.54
+		Hardy	Unionville	193341	5110.44	-14494.71
+		Harmony	Bethel	77060	5652.18	-15038.44
+		Highland Park	Salem	69302	1868.14	-9374.04
+		Hillcrest	Valley View	218569	1675.85	1324.33
+		Hopewell	Centerville	17403	0.00	-6426.77
+		Jackson	Springdale	72874	32.49	-3297.92
+		Jackson	Union Hill	114590	6854.05	-10691.19
+		Jackson	Union Hill	181398	1.53	-2517.92
+		Kingston	Clinton	169584	14529.68	-33013.98
+		Lakeview		65375	123.96	-5494.33
+		Lakeview	Richville	165849	0.00	-25580.39
+		Lakewood	Arlington	113298	5265.99	-6933.54
+		Lakewood	Bridgeport	169124	1332.90	-11714.51
+		Lakewood	Spring Hill	144979	4075.69	-7995.34
+		Lawrenceville	Bunker Hill	237291	9583.05	-5869.08
+		Lebanon	Pomona	55371	11413.39	-9108.39
+		Lewis	Red Hill	43196	0.00	-4774.25
+		Liberty	Spring Hill	95200	703.30	-6668.63
+		Lincoln	Mount Olive	148878	1735.15	-4123.35
+		Lincoln	Shiloh	152927	475.43	-9245.75
+		Ludlow	Shiloh	150308	0.00	2815.96
+		Macedonia	Summerville	96898	190.19	-5906.64
+		Maple Grove	Hardy	94091	74.16	-730.04
+		Maple Grove	Waterloo	225826	3816.54	-8655.39
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q47.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q47.out
new file mode 100644
index 0000000000..f503399dd7
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q47.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q47 --
+Men	importoimporto #1	ese	Unknown	1999	3	5492.81	2070.65	3307.78	2784.11
+Music	exportischolar #1	eing	Unknown	1999	2	5134.23	1740.21	4401.89	2721.61
+Music	edu packscholar #1	ese	Unknown	1999	4	5463.70	2091.07	3391.39	3095.31
+Music	edu packscholar #1	ought	Unknown	1999	7	5552.30	2226.34	4081.99	7573.33
+Music	edu packscholar #1	ese	Unknown	1999	2	5463.70	2268.27	4139.47	3391.39
+Men	edu packimporto #1	ought	Unknown	1999	7	5176.25	1993.73	3542.54	5764.35
+Children	exportiexporti #1	ese	Unknown	1999	3	5185.89	2019.69	3479.21	2982.05
+Shoes	amalgedu pack #1	ation	Unknown	1999	6	5152.09	2077.67	3123.43	2368.73
+Men	edu packimporto #1	eing	Unknown	1999	3	5501.18	2453.88	2683.70	2615.32
+Children	exportiexporti #1	ation	Unknown	1999	4	5238.35	2232.03	3221.70	3649.52
+Shoes	importoedu pack #1	eing	Unknown	1999	5	4698.61	1703.27	2687.15	2757.83
+Music	exportischolar #1	bar	Unknown	1999	7	5318.14	2346.06	3483.88	7658.62
+Music	edu packscholar #1	ation	Unknown	1999	2	5622.22	2657.68	4207.03	3543.82
+Children	exportiexporti #1	bar	Unknown	1999	7	5280.66	2324.79	3003.61	5961.39
+Children	importoexporti #1	eing	Unknown	1999	2	4650.43	1734.45	2341.19	2720.78
+Shoes	importoedu pack #1	ought	Unknown	1999	4	4537.63	1623.33	2928.34	1905.23
+Women	edu packamalg #1	ation	Unknown	1999	4	4507.41	1596.92	2865.38	2665.75
+Men	importoimporto #1	ought	Unknown	1999	6	5045.75	2152.15	2834.94	2667.92
+Men	edu packimporto #1	eing	Unknown	1999	4	5501.18	2615.32	2453.88	2874.96
+Shoes	exportiedu pack #1	bar	Unknown	1999	3	5296.08	2410.44	2685.85	3821.39
+Men	importoimporto #1	able	Unknown	1999	2	5194.81	2333.43	2765.75	2674.91
+Music	amalgscholar #1	ought	Unknown	1999	2	4565.60	1727.16	3895.09	2606.58
+Music	edu packscholar #1	eing	Unknown	1999	5	5539.30	2704.16	3862.34	3960.90
+Shoes	importoedu pack #1	ation	Unknown	1999	4	4690.57	1855.85	3524.69	3050.34
+Men	edu packimporto #1	eing	Unknown	1999	2	5501.18	2683.70	4304.21	2453.88
+Women	exportiamalg #1	able	Unknown	1999	2	4123.60	1306.40	2876.61	2238.71
+Music	exportischolar #1	able	Unknown	1999	5	5090.10	2281.80	2488.24	3304.75
+Shoes	exportiedu pack #1	ought	Unknown	1999	2	4755.53	1951.00	3949.06	2767.05
+Men	importoimporto #1	ese	Unknown	1999	6	5492.81	2691.33	3210.74	3754.33
+Men	amalgimporto #1	ation	Unknown	1999	2	4909.46	2116.43	3187.20	3356.64
+Men	importoimporto #1	ought	Unknown	1999	2	5045.75	2253.70	4158.86	3467.09
+Shoes	amalgedu pack #1	ation	Unknown	1999	7	5152.09	2368.73	2077.67	7543.60
+Men	edu packimporto #1	ese	Unknown	1999	3	4979.36	2201.00	3110.55	3118.30
+Children	exportiexporti #1	ation	Unknown	1999	2	5238.35	2462.96	3808.48	3221.70
+Men	amalgimporto #1	ation	Unknown	1999	4	4909.46	2136.23	3356.64	3046.51
+Children	importoexporti #1	able	Unknown	1999	7	4586.29	1814.46	2643.58	6423.18
+Men	edu packimporto #1	ation	Unknown	1999	4	5170.36	2407.58	3086.77	2492.73
+Shoes	amalgedu pack #1	ese	Unknown	1999	7	4392.18	1630.14	2755.91	6185.73
+Shoes	amalgedu pack #1	able	Unknown	1999	5	4940.22	2187.55	2894.91	3018.65
+Men	edu packimporto #1	ought	Unknown	1999	4	5176.25	2424.94	4285.78	3286.20
+Women	amalgamalg #1	able	Unknown	1999	6	4507.24	1761.81	2891.95	2302.21
+Men	importoimporto #1	ation	Unknown	1999	3	5410.91	2672.68	3591.65	2988.08
+Men	importoimporto #1	ation	Unknown	1999	5	5410.91	2677.81	2988.08	2881.34
+Men	edu packimporto #1	bar	Unknown	1999	4	5632.73	2901.64	3202.87	3447.78
+Children	exportiexporti #1	able	Unknown	1999	5	4955.24	2230.80	2395.57	3003.89
+Men	importoimporto #1	eing	Unknown	1999	5	5074.07	2356.88	2833.40	2854.62
+Men	edu packimporto #1	bar	Unknown	1999	2	5632.73	2916.43	3847.05	3202.87
+Shoes	exportiedu pack #1	ese	Unknown	1999	3	4865.28	2151.76	3212.91	3768.25
+Men	importoimporto #1	ese	Unknown	1999	4	5492.81	2784.11	2070.65	3210.74
+Shoes	exportiedu pack #1	bar	Unknown	1999	7	5296.08	2591.12	3012.98	6254.36
+Shoes	exportiedu pack #1	ation	Unknown	1999	5	4873.51	2170.98	2302.76	3236.50
+Shoes	amalgedu pack #1	able	Unknown	1999	2	4940.22	2239.16	3495.29	2563.93
+Children	exportiexporti #1	eing	Unknown	1999	7	5109.65	2410.24	2916.46	6558.23
+Women	importoamalg #1	able	Unknown	1999	7	4574.74	1881.03	2345.66	6036.28
+Women	amalgamalg #1	ought	Unknown	1999	7	4619.70	1926.67	3528.98	5162.15
+Children	importoexporti #1	bar	Unknown	1999	7	4566.77	1879.57	3400.62	6244.92
+Music	edu packscholar #1	ation	Unknown	1999	6	5622.22	2943.26	3643.42	3847.77
+Music	exportischolar #1	ation	Unknown	1999	4	4962.65	2283.77	3210.01	2539.84
+Music	exportischolar #1	ation	Unknown	1999	2	4962.65	2284.23	3250.69	3210.01
+Men	edu packimporto #1	ation	Unknown	1999	5	5170.36	2492.73	2407.58	3288.94
+Men	edu packimporto #1	able	Unknown	1999	3	4989.30	2318.98	2618.89	3315.77
+Music	exportischolar #1	bar	Unknown	1999	4	5318.14	2651.96	2989.10	3649.76
+Music	exportischolar #1	bar	Unknown	1999	2	5318.14	2656.31	3419.77	2989.10
+Shoes	amalgedu pack #1	bar	Unknown	1999	6	4805.40	2149.56	2686.70	3098.25
+Children	exportiexporti #1	bar	Unknown	1999	4	5280.66	2625.99	3301.62	4331.44
+Music	amalgscholar #1	ation	Unknown	1999	4	4934.50	2280.93	2322.89	2421.13
+Music	edu packscholar #1	able	Unknown	1999	7	5335.90	2684.15	3543.33	7540.94
+Shoes	importoedu pack #1	eing	Unknown	1999	2	4698.61	2058.86	3191.74	2812.15
+Shoes	edu packedu pack #1	ought	Unknown	1999	5	4745.29	2109.27	3203.82	2737.82
+Shoes	importoedu pack #1	ought	Unknown	1999	5	4537.63	1905.23	1623.33	3170.58
+Men	edu packimporto #1	able	Unknown	1999	6	4989.30	2357.82	3363.58	3142.81
+Children	exportiexporti #1	able	Unknown	1999	2	4955.24	2326.67	2746.99	3097.63
+Men	edu packimporto #1	eing	Unknown	1999	5	5501.18	2874.96	2615.32	3714.00
+Shoes	exportiedu pack #1	ation	Unknown	1999	7	4873.51	2256.56	3236.50	6245.37
+Shoes	importoedu pack #1	ese	Unknown	1999	3	4676.74	2060.29	3273.67	2610.86
+Men	amalgimporto #1	ese	Unknown	1999	6	4764.59	2150.16	3284.27	3475.17
+Music	amalgscholar #1	ation	Unknown	1999	3	4934.50	2322.89	3197.76	2280.93
+Shoes	exportiedu pack #1	bar	Unknown	1999	2	5296.08	2685.85	4235.44	2410.44
+Children	importoexporti #1	able	Unknown	1999	5	4586.29	1982.77	2837.74	2643.58
+Music	exportischolar #1	able	Unknown	1999	4	5090.10	2488.24	2966.36	2281.80
+Shoes	importoedu pack #1	able	Unknown	1999	7	4700.91	2100.12	2533.01	5888.57
+Music	edu packscholar #1	bar	Unknown	1999	2	5484.78	2903.45	3410.13	3024.86
+Shoes	exportiedu pack #1	ation	Unknown	1999	3	4873.51	2300.45	2797.30	2302.76
+Shoes	exportiedu pack #1	ation	Unknown	1999	4	4873.51	2302.76	2300.45	2170.98
+Shoes	importoedu pack #1	bar	Unknown	1999	3	4794.67	2225.70	2756.97	2413.76
+Music	edu packscholar #1	ought	Unknown	1999	5	5552.30	2985.49	3241.69	4081.99
+Music	edu packscholar #1	eing	Unknown	1999	3	5539.30	2973.01	3069.18	3862.34
+Shoes	amalgedu pack #1	eing	Unknown	1999	7	4706.54	2143.18	2458.07	5967.73
+Children	exportiexporti #1	ought	Unknown	1999	5	5018.27	2458.03	3467.53	2683.61
+Children	exportiexporti #1	able	Unknown	1999	4	4955.24	2395.57	3097.63	2230.80
+Children	exportiexporti #1	eing	Unknown	1999	2	5109.65	2550.30	4039.10	2685.10
+Music	exportischolar #1	ought	Unknown	1999	4	5079.18	2520.64	3233.50	3079.89
+Women	edu packamalg #1	ation	Unknown	1999	2	4507.41	1951.42	4166.02	2865.38
+Women	amalgamalg #1	ought	Unknown	1999	1	4619.70	2065.94	9639.59	2521.70
+Music	importoscholar #1	ought	Unknown	1999	3	4004.44	1456.84	2438.63	2790.03
+Shoes	edu packedu pack #1	eing	Unknown	1999	5	4664.86	2122.71	3131.02	2852.96
+Women	importoamalg #1	bar	Unknown	1999	3	4437.21	1895.27	2678.48	2999.04
+Music	exportischolar #1	able	Unknown	1999	2	5090.10	2550.48	3702.29	2966.36
+Music	edu packscholar #1	able	Unknown	1999	1	5335.90	2796.97	13360.68	3413.22
+Children	exportiexporti #1	eing	Unknown	1999	4	5109.65	2574.12	2685.10	2672.73
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q48.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q48.out
new file mode 100644
index 0000000000..031d7b8b7f
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q48.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q48 --
+26257
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49.out
new file mode 100644
index 0000000000..370902d527
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49.out
@@ -0,0 +1,35 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q49 --
+catalog	17543	0.5714	1	1
+catalog	14513	0.6354	2	2
+catalog	12577	0.6559	3	3
+catalog	3411	0.7164	4	4
+catalog	361	0.7464	5	5
+catalog	8189	0.7469	6	6
+catalog	8929	0.7625	7	7
+catalog	14869	0.7717	8	8
+catalog	9295	0.7789	9	9
+catalog	16215	0.7906	10	10
+store	9471	0.7750	1	1
+store	9797	0.8000	2	2
+store	12641	0.8160	3	3
+store	15839	0.8163	4	4
+store	1171	0.8241	5	5
+store	11589	0.8265	6	6
+store	6661	0.9220	7	7
+store	13013	0.9420	8	8
+store	14925	0.9647	9	9
+store	4063	1.0000	10	10
+store	9029	1.0000	10	10
+web	7539	0.5900	1	1
+web	3337	0.6265	2	2
+web	15597	0.6619	3	3
+web	2915	0.6986	4	4
+web	11933	0.7171	5	5
+web	3305	0.7375	6	16
+web	483	0.8000	7	6
+web	85	0.8571	8	7
+web	97	0.9036	9	8
+web	117	0.9250	10	9
+web	5299	0.9270	11	10
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.out
new file mode 100644
index 0000000000..3ce76e09ea
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.out
@@ -0,0 +1,35 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q49_rewrite --
+catalog	17543	0.5714	1	1
+catalog	14513	0.6354	2	2
+catalog	12577	0.6559	3	3
+catalog	3411	0.7164	4	4
+catalog	361	0.7464	5	5
+catalog	8189	0.7469	6	6
+catalog	8929	0.7625	7	7
+catalog	14869	0.7717	8	8
+catalog	9295	0.7789	9	9
+catalog	16215	0.7906	10	10
+store	9471	0.7750	1	1
+store	9797	0.8000	2	2
+store	12641	0.8160	3	3
+store	15839	0.8163	4	4
+store	1171	0.8241	5	5
+store	11589	0.8265	6	6
+store	6661	0.9220	7	7
+store	13013	0.9420	8	8
+store	14925	0.9647	9	9
+store	4063	1.0000	10	10
+store	9029	1.0000	10	10
+web	7539	0.5900	1	1
+web	3337	0.6265	2	2
+web	15597	0.6619	3	3
+web	2915	0.6986	4	4
+web	11933	0.7171	5	5
+web	3305	0.7375	6	16
+web	483	0.8000	7	6
+web	85	0.8571	8	7
+web	97	0.9036	9	8
+web	117	0.9250	10	9
+web	5299	0.9270	11	10
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q50.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q50.out
new file mode 100644
index 0000000000..61058f780d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q50.out
@@ -0,0 +1,9 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q50 --
+able	1	255	Sycamore 	Dr.	Suite 410	Midway	Williamson County	TN	31904	67	48	61	66	98
+ation	1	811	Lee 	Circle	Suite T	Midway	Williamson County	TN	31904	70	51	50	61	109
+bar	1	175	4th 	Court	Suite C	Midway	Williamson County	TN	31904	96	53	55	76	86
+eing	1	226	12th 	Lane	Suite D	Fairview	Williamson County	TN	35709	69	63	62	63	114
+ese	1	27	Lake 	Ln	Suite 260	Midway	Williamson County	TN	31904	58	57	55	54	106
+ought	1	767	Spring 	Wy	Suite 250	Midway	Williamson County	TN	31904	81	63	52	58	103
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q51.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q51.out
new file mode 100644
index 0000000000..e25cca840d
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q51.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q51 --
+14	2000-01-10	176.83	\N	176.83	73.60
+14	2000-01-21	\N	75.29	176.83	75.29
+14	2000-01-29	222.33	\N	222.33	75.29
+14	2000-02-02	224.01	\N	224.01	75.29
+14	2000-02-08	\N	85.07	224.01	85.07
+14	2000-02-19	\N	98.60	224.01	98.60
+14	2000-02-21	241.64	\N	241.64	98.60
+14	2000-02-22	\N	99.83	241.64	99.83
+14	2000-03-18	\N	112.82	241.64	112.82
+14	2000-03-23	251.15	\N	251.15	112.82
+14	2000-03-28	260.17	\N	260.17	112.82
+14	2000-03-31	370.74	\N	370.74	112.82
+14	2000-04-05	\N	115.94	370.74	115.94
+14	2000-04-15	445.30	\N	445.30	115.94
+14	2000-04-27	\N	151.48	445.30	151.48
+14	2000-05-03	\N	176.89	445.30	176.89
+14	2000-05-10	451.40	\N	451.40	176.89
+14	2000-05-21	\N	238.39	451.40	238.39
+14	2000-05-26	596.81	\N	596.81	238.39
+14	2000-05-29	\N	242.51	596.81	242.51
+14	2000-06-05	\N	304.64	596.81	304.64
+14	2000-07-03	623.77	\N	623.77	304.64
+14	2000-07-07	\N	307.77	623.77	307.77
+14	2000-07-18	\N	320.04	623.77	320.04
+14	2000-07-25	673.08	\N	673.08	320.04
+14	2000-08-10	\N	411.48	673.08	411.48
+14	2000-08-14	\N	465.85	673.08	465.85
+14	2000-08-21	\N	541.45	673.08	541.45
+14	2000-08-26	\N	574.56	673.08	574.56
+19	2000-01-02	56.96	49.68	56.96	49.68
+25	2000-01-21	97.29	\N	97.29	4.49
+25	2000-01-28	192.46	\N	192.46	4.49
+25	2000-02-09	\N	24.23	192.46	24.23
+25	2000-02-11	\N	98.99	192.46	98.99
+25	2000-02-21	\N	170.60	192.46	170.60
+25	2000-02-22	\N	185.05	192.46	185.05
+35	2000-01-14	\N	55.24	177.88	55.24
+35	2000-01-16	\N	95.92	177.88	95.92
+35	2000-01-18	\N	126.45	177.88	126.45
+35	2000-01-19	\N	167.07	177.88	167.07
+35	2000-02-17	\N	173.97	177.88	173.97
+35	2000-02-22	270.43	\N	270.43	173.97
+35	2000-02-23	\N	180.61	270.43	180.61
+35	2000-03-03	\N	181.99	270.43	181.99
+35	2000-03-05	\N	221.24	270.43	221.24
+35	2000-03-06	\N	266.41	270.43	266.41
+37	2000-01-02	31.75	11.89	31.75	11.89
+37	2000-01-04	\N	17.15	31.75	17.15
+37	2000-01-05	34.34	\N	34.34	17.15
+37	2000-01-06	\N	29.67	34.34	29.67
+41	2000-01-21	\N	15.54	123.34	15.54
+41	2000-02-03	\N	21.04	123.34	21.04
+41	2000-02-16	\N	33.46	123.34	33.46
+41	2000-02-20	\N	37.46	123.34	37.46
+41	2000-02-22	\N	58.57	123.34	58.57
+41	2000-03-05	\N	70.06	123.34	70.06
+41	2000-03-17	178.84	150.76	178.84	150.76
+41	2000-04-26	263.14	\N	263.14	254.88
+41	2000-07-12	474.83	\N	474.83	393.87
+41	2000-07-18	\N	421.23	474.83	421.23
+41	2000-08-15	\N	430.77	474.83	430.77
+49	2000-01-18	\N	2.51	4.58	2.51
+49	2000-01-31	72.47	\N	72.47	13.05
+49	2000-02-13	\N	70.68	72.47	70.68
+49	2000-02-29	\N	71.86	72.47	71.86
+49	2000-04-17	225.29	\N	225.29	219.03
+53	2000-01-02	12.85	1.13	12.85	1.13
+53	2000-01-08	119.24	\N	119.24	1.13
+53	2000-01-09	126.98	\N	126.98	1.13
+53	2000-01-15	\N	3.20	126.98	3.20
+53	2000-02-04	\N	22.89	126.98	22.89
+53	2000-02-05	\N	64.45	126.98	64.45
+53	2000-02-12	\N	66.06	126.98	66.06
+56	2000-01-02	41.57	17.31	41.57	17.31
+61	2000-02-17	421.60	\N	421.60	344.03
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+61	2000-04-22	600.20	\N	600.20	573.28
+71	2000-01-02	13.92	2.88	13.92	2.88
+85	2000-02-03	\N	42.30	65.50	42.30
+85	2000-02-16	\N	42.95	65.50	42.95
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+86	2000-01-19	31.70	\N	31.70	25.97
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+86	2000-02-04	\N	112.15	151.26	112.15
+89	2000-01-12	\N	28.84	181.56	28.84
+89	2000-01-23	\N	67.19	181.56	67.19
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+89	2000-02-25	\N	147.02	181.56	147.02
+89	2000-03-19	\N	172.85	181.56	172.85
+89	2000-03-20	191.66	\N	191.66	172.85
+89	2000-04-11	295.81	\N	295.81	172.85
+89	2000-04-13	\N	203.86	295.81	203.86
+89	2000-04-20	373.30	\N	373.30	203.86
+89	2000-04-23	\N	219.74	373.30	219.74
+89	2000-04-26	\N	235.97	373.30	235.97
+89	2000-05-04	\N	248.05	373.30	248.05
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q52.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q52.out
new file mode 100644
index 0000000000..632da0643f
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q52.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q52 --
+2000	2001002	amalgimporto #2	133668.20
+2000	1003001	exportiamalg #1	85038.62
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q53.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q53.out
new file mode 100644
index 0000000000..fb1a250c2a
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q53.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q53 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q54.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q54.out
new file mode 100644
index 0000000000..2d139af560
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q54.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q54 --
+11860	1	593000
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q55.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q55.out
new file mode 100644
index 0000000000..47107364dc
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q55.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q55 --
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q56.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q56.out
new file mode 100644
index 0000000000..2272b8a483
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q56.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q56 --
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+AAAAAAAAEKLBAAAA	572.32
+AAAAAAAAAHCBAAAA	604.38
+AAAAAAAALFADAAAA	606.67
+AAAAAAAAKFNDAAAA	617.96
+AAAAAAAAOEKAAAAA	619.39
+AAAAAAAAEEBEAAAA	626.40
+AAAAAAAAKMBCAAAA	628.95
+AAAAAAAAJHGDAAAA	631.81
+AAAAAAAAOMLDAAAA	631.89
+AAAAAAAAGNDDAAAA	645.99
+AAAAAAAAEADAAAAA	648.20
+AAAAAAAAKPKCAAAA	651.42
+AAAAAAAAAKHCAAAA	657.04
+AAAAAAAAOEIDAAAA	660.24
+AAAAAAAAMKAEAAAA	691.02
+AAAAAAAABLKAAAAA	691.26
+AAAAAAAADEIBAAAA	726.72
+AAAAAAAAKBHCAAAA	730.20
+AAAAAAAADNJAAAAA	731.92
+AAAAAAAAOFPBAAAA	737.28
+AAAAAAAACPIBAAAA	737.64
+AAAAAAAAEPPBAAAA	759.36
+AAAAAAAALNHDAAAA	761.60
+AAAAAAAAGKPDAAAA	773.56
+AAAAAAAAKBCAAAAA	775.30
+AAAAAAAAIBOCAAAA	777.48
+AAAAAAAAOJBEAAAA	777.84
+AAAAAAAAKFKBAAAA	780.41
+AAAAAAAAPJCCAAAA	783.00
+AAAAAAAACAKBAAAA	800.27
+AAAAAAAAOFNDAAAA	806.19
+AAAAAAAAGLIDAAAA	843.75
+AAAAAAAAGDBAAAAA	868.77
+AAAAAAAAOGFDAAAA	887.30
+AAAAAAAACOLBAAAA	918.96
+AAAAAAAAFFNCAAAA	923.35
+AAAAAAAACCJDAAAA	955.08
+AAAAAAAAMMCAAAAA	959.76
+AAAAAAAACCPDAAAA	971.77
+AAAAAAAAKNCEAAAA	998.60
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q57.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q57.out
new file mode 100644
index 0000000000..4e58852f68
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q57.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q57 --
+Shoes	importoedu pack #1	North Midwest	1999	3	7101.78	2518.33	5264.86	3066.29
+Men	edu packimporto #1	NY Metro	1999	4	7659.24	3227.88	4759.01	4664.83
+Music	amalgscholar #1	Mid Atlantic	1999	1	6659.49	2291.60	14178.45	4267.08
+Men	edu packimporto #1	NY Metro	1999	7	7659.24	3327.90	4566.86	11656.06
+Men	importoimporto #1	North Midwest	1999	5	7640.49	3327.30	3534.12	5530.63
+Music	edu packscholar #1	Mid Atlantic	1999	1	8223.71	3965.88	14493.32	4943.68
+Women	amalgamalg #1	Mid Atlantic	1999	2	7116.32	2872.43	4945.20	2974.14
+Music	exportischolar #1	NY Metro	1999	3	7047.80	2832.46	4308.87	3265.21
+Children	importoexporti #1	NY Metro	1999	4	6809.59	2648.85	5318.02	4111.73
+Children	importoexporti #1	Mid Atlantic	1999	5	6832.82	2687.17	3971.76	4235.84
+Music	edu packscholar #1	North Midwest	1999	1	8078.69	3934.06	17002.72	4244.46
+Women	amalgamalg #1	Mid Atlantic	1999	3	7116.32	2974.14	2872.43	4329.06
+Music	edu packscholar #1	Mid Atlantic	1999	4	8223.71	4091.54	5753.94	4797.32
+Men	importoimporto #1	NY Metro	1999	2	7530.99	3406.53	5839.59	6125.77
+Men	edu packimporto #1	North Midwest	1999	3	7852.62	3737.23	4465.92	3831.92
+Shoes	edu packedu pack #1	North Midwest	1999	5	6583.30	2474.01	3684.23	4335.58
+Men	importoimporto #1	North Midwest	1999	4	7640.49	3534.12	4291.30	3327.30
+Children	amalgexporti #1	NY Metro	1999	7	6518.75	2430.77	3203.23	7867.12
+Music	amalgscholar #1	North Midwest	1999	5	6876.88	2816.26	4637.07	3528.80
+Shoes	importoedu pack #1	North Midwest	1999	4	7101.78	3066.29	2518.33	5005.85
+Shoes	edu packedu pack #1	NY Metro	1999	2	6421.79	2394.57	3149.35	4472.43
+Music	edu packscholar #1	NY Metro	1999	7	7966.57	3944.74	5030.35	10791.61
+Men	edu packimporto #1	North Midwest	1999	4	7852.62	3831.92	3737.23	4353.90
+Shoes	importoedu pack #1	Mid Atlantic	1999	3	6959.93	2951.09	4142.26	3271.07
+Men	amalgimporto #1	NY Metro	1999	7	7082.12	3075.18	5231.88	8953.66
+Shoes	amalgedu pack #1	NY Metro	1999	7	6904.68	2902.26	4942.02	8793.71
+Men	importoimporto #1	Mid Atlantic	1999	1	7357.06	3356.15	11222.19	3770.17
+Children	exportiexporti #1	NY Metro	1999	7	7698.34	3714.25	4686.40	9752.38
+Children	exportiexporti #1	North Midwest	1999	3	7530.37	3586.50	3960.74	3916.86
+Children	amalgexporti #1	NY Metro	1999	4	6518.75	2587.68	4177.93	3342.60
+Music	edu packscholar #1	North Midwest	1999	5	8078.69	4148.83	5184.39	5483.81
+Children	exportiexporti #1	Mid Atlantic	1999	3	7245.28	3350.65	3876.83	5869.66
+Children	exportiexporti #1	North Midwest	1999	1	7530.37	3645.95	13367.51	3960.74
+Shoes	exportiedu pack #1	Mid Atlantic	1999	2	6885.32	3013.95	4139.82	4328.03
+Children	importoexporti #1	North Midwest	1999	7	6690.09	2827.63	3965.68	7733.16
+Music	edu packscholar #1	North Midwest	1999	2	8078.69	4244.46	3934.06	4448.50
+Men	edu packimporto #1	Mid Atlantic	1999	1	7912.53	4082.68	14333.30	4383.51
+Children	exportiexporti #1	NY Metro	1999	3	7698.34	3883.01	4723.97	4590.03
+Music	exportischolar #1	Mid Atlantic	1999	2	7243.99	3430.04	3662.97	3617.09
+Music	exportischolar #1	NY Metro	1999	1	7047.80	3237.56	15805.49	4308.87
+Music	exportischolar #1	NY Metro	1999	4	7047.80	3265.21	2832.46	3885.17
+Music	exportischolar #1	North Midwest	1999	2	7593.92	3821.24	4748.20	4271.45
+Men	edu packimporto #1	Mid Atlantic	1999	4	7912.53	4144.67	4954.05	5070.06
+Music	amalgscholar #1	NY Metro	1999	4	6926.06	3165.20	4688.23	4286.05
+Shoes	amalgedu pack #1	Mid Atlantic	1999	3	6642.08	2928.82	3648.57	3892.32
+Music	edu packscholar #1	NY Metro	1999	3	7966.57	4269.89	4384.51	4452.73
+Men	amalgimporto #1	NY Metro	1999	2	7082.12	3392.25	4549.47	3653.84
+Shoes	importoedu pack #1	Mid Atlantic	1999	4	6959.93	3271.07	2951.09	4231.88
+Music	amalgscholar #1	North Midwest	1999	3	6876.88	3190.36	3536.29	4637.07
+Women	importoamalg #1	Mid Atlantic	1999	1	6479.29	2804.94	13543.31	3515.21
+Shoes	amalgedu pack #1	North Midwest	1999	6	6829.95	3178.20	4120.60	5910.98
+Men	amalgimporto #1	NY Metro	1999	4	7082.12	3450.11	3653.84	5965.16
+Women	edu packamalg #1	NY Metro	1999	4	6608.48	2976.95	3489.35	3812.22
+Music	edu packscholar #1	North Midwest	1999	3	8078.69	4448.50	4244.46	5184.39
+Music	amalgscholar #1	Mid Atlantic	1999	7	6659.49	3031.62	4214.81	9493.69
+Music	exportischolar #1	Mid Atlantic	1999	3	7243.99	3617.09	3430.04	3871.67
+Men	importoimporto #1	NY Metro	1999	7	7530.99	3913.80	4405.46	7859.96
+Children	exportiexporti #1	North Midwest	1999	4	7530.37	3916.86	3586.50	4747.06
+Children	importoexporti #1	NY Metro	1999	2	6809.59	3200.10	4421.85	5318.02
+Men	importoimporto #1	Mid Atlantic	1999	2	7357.06	3770.17	3356.15	5114.32
+Children	amalgexporti #1	North Midwest	1999	2	6557.57	2975.00	3418.40	5079.56
+Music	edu packscholar #1	NY Metro	1999	2	7966.57	4384.51	5279.09	4269.89
+Music	exportischolar #1	Mid Atlantic	1999	1	7243.99	3662.97	14285.88	3430.04
+Children	exportiexporti #1	North Midwest	1999	2	7530.37	3960.74	3645.95	3586.50
+Men	amalgimporto #1	Mid Atlantic	1999	1	6611.33	3066.19	11053.20	3203.24
+Women	edu packamalg #1	Mid Atlantic	1999	4	6061.21	2522.22	2674.89	3975.90
+Shoes	exportiedu pack #1	North Midwest	1999	4	7045.16	3509.17	5252.01	3987.99
+Men	edu packimporto #1	North Midwest	1999	1	7852.62	4318.23	16397.10	4465.92
+Men	edu packimporto #1	Mid Atlantic	1999	2	7912.53	4383.51	4082.68	4954.05
+Music	edu packscholar #1	NY Metro	1999	4	7966.57	4452.73	4269.89	5476.13
+Children	importoexporti #1	Mid Atlantic	1999	1	6832.82	3330.21	13097.88	3496.46
+Men	edu packimporto #1	North Midwest	1999	5	7852.62	4353.90	3831.92	5689.03
+Women	exportiamalg #1	Mid Atlantic	1999	2	6013.08	2536.08	3406.41	3718.65
+Music	exportischolar #1	North Midwest	1999	4	7593.92	4121.47	4271.45	5234.85
+Music	amalgscholar #1	North Midwest	1999	1	6876.88	3406.89	13714.11	3536.29
+Men	importoimporto #1	NY Metro	1999	4	7530.99	4062.17	6125.77	4715.58
+Men	exportiimporto #1	North Midwest	1999	5	5797.38	2330.52	2965.11	2842.02
+Children	edu packexporti #1	Mid Atlantic	1999	3	6100.64	2633.95	3676.43	3130.51
+Men	amalgimporto #1	North Midwest	1999	1	6713.82	3268.58	13596.45	4098.03
+Women	exportiamalg #1	NY Metro	1999	4	6031.04	2588.60	3554.29	3915.46
+Men	amalgimporto #1	NY Metro	1999	3	7082.12	3653.84	3392.25	3450.11
+Music	edu packscholar #1	Mid Atlantic	1999	5	8223.71	4797.32	4091.54	5028.71
+Children	exportiexporti #1	North Midwest	1999	6	7530.37	4104.53	4747.06	4586.73
+Women	importoamalg #1	NY Metro	1999	4	6352.68	2928.62	3718.55	3387.14
+Men	edu packimporto #1	NY Metro	1999	2	7659.24	4246.37	4489.63	4759.01
+Shoes	edu packedu pack #1	Mid Atlantic	1999	3	6578.78	3166.49	4213.93	4376.74
+Shoes	amalgedu pack #1	North Midwest	1999	4	6829.95	3417.69	3722.75	4120.60
+Women	exportiamalg #1	North Midwest	1999	3	6171.54	2760.78	3853.84	3125.47
+Men	amalgimporto #1	Mid Atlantic	1999	2	6611.33	3203.24	3066.19	4613.61
+Men	edu packimporto #1	North Midwest	1999	2	7852.62	4465.92	4318.23	3737.23
+Women	edu packamalg #1	Mid Atlantic	1999	3	6061.21	2674.89	3376.95	2522.22
+Music	exportischolar #1	NY Metro	1999	6	7047.80	3667.60	3885.17	5088.70
+Children	importoexporti #1	North Midwest	1999	3	6690.09	3310.89	3486.35	5014.28
+Music	exportischolar #1	Mid Atlantic	1999	4	7243.99	3871.67	3617.09	4628.08
+Men	importoimporto #1	North Midwest	1999	2	7640.49	4270.44	4931.98	4291.30
+Children	exportiexporti #1	Mid Atlantic	1999	2	7245.28	3876.83	4799.92	3350.65
+Shoes	exportiedu pack #1	North Midwest	1999	7	7045.16	3684.37	4372.20	8403.23
+Men	importoimporto #1	North Midwest	1999	3	7640.49	4291.30	4270.44	3534.12
+Men	amalgimporto #1	Mid Atlantic	1999	4	6611.33	3262.24	4613.61	4531.38
+Music	amalgscholar #1	North Midwest	1999	6	6876.88	3528.80	2816.26	4750.85
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q58.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q58.out
new file mode 100644
index 0000000000..991a7e47c9
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q58.out
@@ -0,0 +1,6 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q58 --
+AAAAAAAACNGBAAAA	1900.15	11.00	1950.92	11.00	1829.52	10.00	1893.53
+AAAAAAAAIDOAAAAA	6605.22	11.00	6078.33	10.00	6338.25	11.00	6340.60
+AAAAAAAAJMFCAAAA	3608.52	11.00	3590.47	11.00	3305.82	10.00	3501.60
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q59.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q59.out
new file mode 100644
index 0000000000..de18bdaa1e
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q59.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q59 --
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5271	1.36	3.08	0.32	0.48	\N	0.75	1.84
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5272	1.09	0.75	0.68	0.98	\N	1.33	0.99
+able	AAAAAAAACAAAAAAA	5273	1.19	1.02	0.47	0.63	\N	1.49	0.57
+able	AAAAAAAACAAAAAAA	5273	1.19	1.02	0.47	0.63	\N	1.49	0.57
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q60.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q60.out
new file mode 100644
index 0000000000..c5347b4782
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q60.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q60 --
+AAAAAAAAAABBAAAA	6287.84
+AAAAAAAAAABEAAAA	1297.26
+AAAAAAAAAADCAAAA	8936.88
+AAAAAAAAAADEAAAA	20945.84
+AAAAAAAAAAHAAAAA	7227.38
+AAAAAAAAAANDAAAA	17117.56
+AAAAAAAAAAOCAAAA	4287.89
+AAAAAAAAABADAAAA	499.72
+AAAAAAAAABBCAAAA	22704.66
+AAAAAAAAABCEAAAA	22499.76
+AAAAAAAAABDEAAAA	5527.74
+AAAAAAAAABFBAAAA	4953.67
+AAAAAAAAABFEAAAA	12523.22
+AAAAAAAAABHCAAAA	20093.49
+AAAAAAAAABIBAAAA	382.30
+AAAAAAAAABICAAAA	1271.62
+AAAAAAAAABKCAAAA	7355.85
+AAAAAAAAABLBAAAA	786.05
+AAAAAAAAABNAAAAA	8385.94
+AAAAAAAAACACAAAA	11564.85
+AAAAAAAAACBEAAAA	13046.23
+AAAAAAAAACCDAAAA	14533.50
+AAAAAAAAACDCAAAA	820.49
+AAAAAAAAACLBAAAA	6761.17
+AAAAAAAAACNCAAAA	19023.15
+AAAAAAAAACODAAAA	7322.65
+AAAAAAAAADACAAAA	4696.41
+AAAAAAAAADBAAAAA	988.80
+AAAAAAAAADDCAAAA	5661.21
+AAAAAAAAADDEAAAA	1846.56
+AAAAAAAAADEEAAAA	15327.48
+AAAAAAAAADGEAAAA	4511.21
+AAAAAAAAADHDAAAA	27866.80
+AAAAAAAAADICAAAA	15624.49
+AAAAAAAAADLCAAAA	399.00
+AAAAAAAAADNDAAAA	6403.89
+AAAAAAAAADPCAAAA	4246.28
+AAAAAAAAAEBAAAAA	1946.34
+AAAAAAAAAEBCAAAA	2021.69
+AAAAAAAAAEBDAAAA	6664.64
+AAAAAAAAAECCAAAA	11363.72
+AAAAAAAAAECEAAAA	10198.96
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+AAAAAAAAAGDCAAAA	2495.73
+AAAAAAAAAGDEAAAA	6750.35
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+AAAAAAAAAGHAAAAA	17532.15
+AAAAAAAAAGHDAAAA	1220.67
+AAAAAAAAAGJCAAAA	7564.93
+AAAAAAAAAGKAAAAA	14082.90
+AAAAAAAAAGKDAAAA	1891.00
+AAAAAAAAAGNBAAAA	7856.78
+AAAAAAAAAGOCAAAA	14920.31
+AAAAAAAAAGPCAAAA	7986.28
+AAAAAAAAAHCBAAAA	15074.94
+AAAAAAAAAHECAAAA	11667.69
+AAAAAAAAAHHDAAAA	2491.48
+AAAAAAAAAHJDAAAA	4056.95
+AAAAAAAAAHMAAAAA	15866.43
+AAAAAAAAAHNDAAAA	7289.53
+AAAAAAAAAIACAAAA	2603.30
+AAAAAAAAAIBCAAAA	9.09
+AAAAAAAAAIBEAAAA	6979.53
+AAAAAAAAAIDAAAAA	32.70
+AAAAAAAAAIDCAAAA	15307.29
+AAAAAAAAAIIAAAAA	11268.14
+AAAAAAAAAIIDAAAA	6837.36
+AAAAAAAAAIJCAAAA	28318.16
+AAAAAAAAAIMAAAAA	4013.06
+AAAAAAAAAINBAAAA	7691.92
+AAAAAAAAAINCAAAA	13190.42
+AAAAAAAAAIODAAAA	17489.85
+AAAAAAAAAIPDAAAA	6096.92
+AAAAAAAAAJAEAAAA	30169.62
+AAAAAAAAAJCCAAAA	17608.54
+AAAAAAAAAJGBAAAA	14.10
+AAAAAAAAAJJCAAAA	2763.55
+AAAAAAAAAJMBAAAA	1013.43
+AAAAAAAAAJMCAAAA	12220.00
+AAAAAAAAAJNBAAAA	8076.70
+AAAAAAAAAJODAAAA	5113.02
+AAAAAAAAAKBAAAAA	2413.44
+AAAAAAAAAKDAAAAA	15712.05
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q61.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q61.out
new file mode 100644
index 0000000000..314742d538
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q61.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q61 --
+2894229.70	5493575.41	52.6800
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q62.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q62.out
new file mode 100644
index 0000000000..9a738814a6
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q62.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q62 --
+	EXPRESS	site_0	260	283	296	314	0
+	EXPRESS	site_1	330	285	315	329	0
+	EXPRESS	site_2	283	291	318	333	0
+	EXPRESS	site_3	274	280	277	258	0
+	EXPRESS	site_4	247	244	268	259	0
+	LIBRARY	site_0	197	180	203	180	0
+	LIBRARY	site_1	208	205	185	211	0
+	LIBRARY	site_2	258	230	267	243	0
+	LIBRARY	site_3	243	204	241	252	0
+	LIBRARY	site_4	211	213	216	206	0
+	NEXT DAY	site_0	332	290	340	289	0
+	NEXT DAY	site_1	297	292	275	334	0
+	NEXT DAY	site_2	287	253	272	220	0
+	NEXT DAY	site_3	255	252	243	264	0
+	NEXT DAY	site_4	300	309	309	281	0
+	OVERNIGHT	site_0	242	224	226	237	0
+	OVERNIGHT	site_1	237	202	213	204	0
+	OVERNIGHT	site_2	172	182	158	181	0
+	OVERNIGHT	site_3	217	193	201	175	0
+	OVERNIGHT	site_4	231	251	249	237	0
+	REGULAR	site_0	227	216	199	193	0
+	REGULAR	site_1	239	209	254	246	0
+	REGULAR	site_2	235	241	259	219	0
+	REGULAR	site_3	204	212	185	237	0
+	REGULAR	site_4	171	176	182	163	0
+	TWO DAY	site_0	189	201	202	218	0
+	TWO DAY	site_1	199	170	177	174	0
+	TWO DAY	site_2	200	202	181	179	0
+	TWO DAY	site_3	237	234	233	253	0
+	TWO DAY	site_4	233	229	234	238	0
+Bad cards must make.	EXPRESS	site_0	251	248	268	277	0
+Bad cards must make.	EXPRESS	site_1	262	304	290	267	0
+Bad cards must make.	EXPRESS	site_2	287	290	293	286	0
+Bad cards must make.	EXPRESS	site_3	283	260	267	290	0
+Bad cards must make.	EXPRESS	site_4	281	310	309	321	0
+Bad cards must make.	LIBRARY	site_0	254	265	226	231	0
+Bad cards must make.	LIBRARY	site_1	198	231	211	201	0
+Bad cards must make.	LIBRARY	site_2	181	172	190	176	0
+Bad cards must make.	LIBRARY	site_3	196	214	173	226	0
+Bad cards must make.	LIBRARY	site_4	241	241	247	236	0
+Bad cards must make.	NEXT DAY	site_0	259	255	279	260	0
+Bad cards must make.	NEXT DAY	site_1	287	334	295	300	0
+Bad cards must make.	NEXT DAY	site_2	269	309	306	272	0
+Bad cards must make.	NEXT DAY	site_3	281	322	308	277	0
+Bad cards must make.	NEXT DAY	site_4	273	295	275	281	0
+Bad cards must make.	OVERNIGHT	site_0	221	216	201	213	0
+Bad cards must make.	OVERNIGHT	site_1	250	228	257	235	0
+Bad cards must make.	OVERNIGHT	site_2	255	223	230	238	0
+Bad cards must make.	OVERNIGHT	site_3	195	217	199	211	0
+Bad cards must make.	OVERNIGHT	site_4	175	165	169	164	0
+Bad cards must make.	REGULAR	site_0	176	239	220	233	0
+Bad cards must make.	REGULAR	site_1	187	163	200	198	0
+Bad cards must make.	REGULAR	site_2	190	231	195	208	0
+Bad cards must make.	REGULAR	site_3	227	241	231	252	0
+Bad cards must make.	REGULAR	site_4	258	245	260	245	0
+Bad cards must make.	TWO DAY	site_0	222	235	235	223	0
+Bad cards must make.	TWO DAY	site_1	264	248	237	227	0
+Bad cards must make.	TWO DAY	site_2	181	230	231	207	0
+Bad cards must make.	TWO DAY	site_3	182	158	209	185	0
+Bad cards must make.	TWO DAY	site_4	203	218	198	221	0
+Conventional childr	EXPRESS	site_0	311	308	336	287	0
+Conventional childr	EXPRESS	site_1	250	269	273	260	0
+Conventional childr	EXPRESS	site_2	255	280	235	276	0
+Conventional childr	EXPRESS	site_3	269	292	303	278	0
+Conventional childr	EXPRESS	site_4	275	324	268	330	0
+Conventional childr	LIBRARY	site_0	212	211	225	250	0
+Conventional childr	LIBRARY	site_1	218	249	244	204	0
+Conventional childr	LIBRARY	site_2	203	215	222	205	0
+Conventional childr	LIBRARY	site_3	163	181	180	193	0
+Conventional childr	LIBRARY	site_4	189	208	207	191	0
+Conventional childr	NEXT DAY	site_0	253	269	285	258	0
+Conventional childr	NEXT DAY	site_1	289	244	254	269	0
+Conventional childr	NEXT DAY	site_2	341	290	310	315	0
+Conventional childr	NEXT DAY	site_3	309	294	328	270	0
+Conventional childr	NEXT DAY	site_4	312	311	275	291	0
+Conventional childr	OVERNIGHT	site_0	180	193	167	211	0
+Conventional childr	OVERNIGHT	site_1	196	210	238	194	0
+Conventional childr	OVERNIGHT	site_2	218	236	287	220	0
+Conventional childr	OVERNIGHT	site_3	235	253	228	246	0
+Conventional childr	OVERNIGHT	site_4	211	199	201	201	0
+Conventional childr	REGULAR	site_0	209	262	232	207	0
+Conventional childr	REGULAR	site_1	214	218	222	227	0
+Conventional childr	REGULAR	site_2	166	193	184	183	0
+Conventional childr	REGULAR	site_3	195	191	221	219	0
+Conventional childr	REGULAR	site_4	248	236	244	256	0
+Conventional childr	TWO DAY	site_0	192	225	232	225	0
+Conventional childr	TWO DAY	site_1	248	239	233	251	0
+Conventional childr	TWO DAY	site_2	230	214	237	201	0
+Conventional childr	TWO DAY	site_3	236	190	190	222	0
+Conventional childr	TWO DAY	site_4	197	159	202	176	0
+Doors canno	EXPRESS	site_0	303	312	304	319	0
+Doors canno	EXPRESS	site_1	321	278	313	297	0
+Doors canno	EXPRESS	site_2	267	277	265	257	0
+Doors canno	EXPRESS	site_3	263	245	258	285	0
+Doors canno	EXPRESS	site_4	282	313	319	323	0
+Doors canno	LIBRARY	site_0	201	210	201	200	0
+Doors canno	LIBRARY	site_1	239	217	208	206	0
+Doors canno	LIBRARY	site_2	229	233	244	241	0
+Doors canno	LIBRARY	site_3	216	198	182	222	0
+Doors canno	LIBRARY	site_4	176	163	170	216	0
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q63.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q63.out
new file mode 100644
index 0000000000..0d58f5c794
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q63.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q63 --
+1	189.04	531.91
+1	203.73	531.91
+1	355.17	531.91
+1	359.53	531.91
+1	365.84	531.91
+1	373.26	531.91
+1	430.03	531.91
+1	698.60	531.91
+1	909.18	531.91
+1	995.02	531.91
+1	998.95	531.91
+2	34.79	625.03
+2	143.64	625.03
+2	253.85	625.03
+2	359.05	625.03
+2	445.59	625.03
+2	499.99	625.03
+2	538.36	625.03
+2	780.07	625.03
+2	1487.82	625.03
+2	1660.48	625.03
+3	270.10	988.29
+3	367.94	988.29
+3	491.20	988.29
+3	529.42	988.29
+3	629.34	988.29
+3	757.70	988.29
+3	1161.47	988.29
+3	1234.30	988.29
+3	1251.01	988.29
+3	1522.49	988.29
+3	2624.04	988.29
+5	353.24	1169.48
+5	581.28	1169.48
+5	694.79	1169.48
+5	696.66	1169.48
+5	740.99	1169.48
+5	890.86	1169.48
+5	955.61	1169.48
+5	1359.61	1169.48
+5	1712.35	1169.48
+5	1794.66	1169.48
+5	2080.16	1169.48
+5	2173.58	1169.48
+6	644.61	1772.99
+6	851.23	1772.99
+6	868.62	1772.99
+6	1056.06	1772.99
+6	1150.40	1772.99
+6	1196.50	1772.99
+6	1551.15	1772.99
+6	2576.64	1772.99
+6	2875.54	1772.99
+6	3299.70	1772.99
+6	3309.13	1772.99
+7	638.30	1499.65
+7	663.54	1499.65
+7	779.65	1499.65
+7	800.25	1499.65
+7	811.54	1499.65
+7	987.78	1499.65
+7	1046.24	1499.65
+7	1667.92	1499.65
+7	2750.32	1499.65
+7	2970.80	1499.65
+7	3524.00	1499.65
+8	803.53	1618.95
+8	819.18	1618.95
+8	933.76	1618.95
+8	944.19	1618.95
+8	1019.24	1618.95
+8	1159.06	1618.95
+8	1290.94	1618.95
+8	2168.17	1618.95
+8	2264.18	1618.95
+8	3033.41	1618.95
+8	3213.77	1618.95
+9	315.31	1509.97
+9	559.82	1509.97
+9	728.72	1509.97
+9	784.15	1509.97
+9	923.36	1509.97
+9	933.68	1509.97
+9	1239.49	1509.97
+9	1848.85	1509.97
+9	2172.04	1509.97
+9	2178.07	1509.97
+9	3165.98	1509.97
+9	3270.26	1509.97
+10	314.30	1125.25
+10	365.57	1125.25
+10	591.30	1125.25
+10	760.81	1125.25
+10	776.91	1125.25
+10	959.65	1125.25
+10	975.25	1125.25
+10	1294.20	1125.25
+10	1342.05	1125.25
+10	1914.64	1125.25
+10	3105.53	1125.25
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q64.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q64.out
new file mode 100644
index 0000000000..9c3dd65c40
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q64.out
@@ -0,0 +1,13 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q64 --
+n stableableantiought	able	31904	987	Hillcrest 	Fairbanks	46653	216	3rd 	Reno	40344	1999	1	55.42	80.91	954.53	59.08	76.80	0.00	2000	1
+n stableableantiought	ation	31904	425	Green 	Enterprise	11757	772	Valley 			1999	1	16.24	25.17	0.00	7.92	11.80	0.00	2000	1
+n stableableantiought	ation	31904	425	Green 	Enterprise	11757	772	Valley 			1999	1	16.24	25.17	0.00	44.27	58.43	0.00	2000	1
+n stableableantiought	ation	31904	316	Valley Tenth	Pine Grove	74593	806	Wilson Main	Jackson	59583	1999	1	49.63	78.41	14.22	7.92	11.80	0.00	2000	1
+n stableableantiought	ation	31904	316	Valley Tenth	Pine Grove	74593	806	Wilson Main	Jackson	59583	1999	1	49.63	78.41	14.22	44.27	58.43	0.00	2000	1
+n stableableantiought	ation	31904	173	Park Maple	Sulphur Springs	68354	232	Franklin 	Fairfield	66192	1999	1	60.78	85.09	0.00	7.92	11.80	0.00	2000	1
+n stableableantiought	ation	31904	173	Park Maple	Sulphur Springs	68354	232	Franklin 	Fairfield	66192	1999	1	60.78	85.09	0.00	44.27	58.43	0.00	2000	1
+n stableableantiought	eing	35709	928	First Oak	Summit	40499	178	Johnson Hillcrest	Oakdale	59584	1999	1	63.82	121.25	0.00	10.25	12.09	0.00	2000	1
+n stableableantiought	eing	35709	928	First Oak	Summit	40499	178	Johnson Hillcrest	Oakdale	59584	1999	1	63.82	121.25	0.00	30.28	49.65	0.00	2000	1
+n stableableantiought	eing	35709	928	First Oak	Summit	40499	178	Johnson Hillcrest	Oakdale	59584	1999	1	63.82	121.25	0.00	58.19	85.53	0.00	2000	1
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q65.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q65.out
new file mode 100644
index 0000000000..d330aa354f
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q65.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q65 --
+able	Commercial, popular processes give later now wooden facts. Black, outer purposes examine national, precise heels. Invisible times s	36.06	5.25	1.68	amalgedu pack #1
+able	Conventional, responsible products discuss delicately then actual findings. Extremel	36.42	3.67	2.82	namelessnameless #5
+able	Difficulties wou	25.25	6.61	2.31	importobrand #7
+able	Efforts might come ever. Even agricultural operations live plus a members. Arts may ring too carefully numerous windows. In particular new ch	22.13	1.49	0.95	edu packamalg #2
+able	Even chief games can present privately extra whole steps. Over old reasons ought to 	21.58	4.30	1.41	namelesscorp #3
+able	Initial, important ministers used to rely. Young, difficult glasses cannot say european, religious organisations; worried minutes protect action	11.03	4.95	3.06	scholarnameless #7
+able	Once olympic sorts could control governments. Final sections mig	21.77	7.76	4.42	edu packscholar #1
+able	Small others will provide probably complete countries. Only effective pensions violate on board maybe left animals. Old r	26.38	2.55	1.60	maxiunivamalg #2
+able	Somehow revolutionary sh	31.09	7.10	3.05	amalgunivamalg #3
+able	Stupid years should not modify loose. Special, sure developments may	7.84	5.81	4.47	maxicorp #5
+able	Swiss, capable lovers handle at least present details. Over here uncomfortable stations take by a lips. Private organisations can supply in a examinatio	29.73	8.15	3.99	amalgedu pack #2
+able	Toys must not meet again goods. Comfortable, major members last days. Popular, double occupations may compensate researchers. Conditions 	25.02	3.35	2.41	edu packscholar #1
+able	Wrong, good values could not work in a minds. Normal structures correct here later massive boo	21.49	4.17	1.45	importoedu pack #1
+ation	Animals conduct primary, sorry tickets. Bare, labour matters used to buy between a patterns. Light authorities explain rules; future eggs can 	1.57	0.86	0.47	edu packamalg #1
+ation	Average services could try unfortunately plants; extensive procedures must	34.63	4.94	3.26	scholarunivamalg #2
+ation	Direct ways topple inches. Complex years can take also across a words. Stupid, global implications would provide sure things. Russians take much for the spots. Indoors local men w	16.06	7.18	4.88	exportiedu pack #1
+ation	Disciplinary signs could prepare particular, other councils. Live criteria ought to stand extra, basic bones. Local leaders tell now tasks. Most old recordings use ago in the governors; par	36.65	8.96	4.39	amalgscholar #2
+ation	Fair families ought to leave relationships. Separate, firm services would assist delicious, serious laws; a	15.31	7.51	2.70	importoamalg #1
+ation	Gradually general affairs come at a gardens. Members may liv	18.43	28.90	11.27	edu packamalgamalg #15
+ation	Here equivalent expectations should stop since to th	18.23	7.88	5.04	scholarunivamalg #4
+ation	Laws propose policies. Commercial, foreign restaurants could take. District	29.49	84.97	32.28	amalgnameless #5
+ation	New r	36.57	2.10	1.19	importoimporto #2
+ation	Officials calculate in the images. Military, olympic services throw apparently old photographs; exotic, wonderful children benefit	25.41	9.36	3.55	maxibrand #9
+ation	Open blue farmers reach useful, old arrangements. American, short years reach now tender, heavy neighbours. Now top boundaries would not enable emotions. Effectively specific 	28.75	2.34	2.01	exportinameless #7
+ation	Or	23.64	3.00	1.86	amalgedu pack #1
+ation	Pensions used to meet in the words. Very african obligati	27.27	0.54	0.27	exportiexporti #1
+ation	Public, limited pup	34.60	9.38	3.93	amalgmaxi #9
+ation	Small kinds would recognize notably violent, labour years. Electronic days would not	30.96	0.90	0.34	namelesscorp #3
+ation	Social, other resources may know reasonable, distant weeks. New, unexpected rates mean. White, electric generations carry together other t	36.10	3.91	2.54	maxinameless #10
+ation	Trustees grow well thereby national attitudes. Social, excellent bacteria contain permanent gaps. Only dynamic uses ought to halt very long, bright men; japanese, distin	3.96	3.31	2.87	exporticorp #1
+bar	Categories shall	18.32	8.98	7.81	scholarmaxi #9
+bar	Clear, top associations can activate all national factors. Items could think sure skills. Fine, thin classes must not help simply only statutory	28.26	6.27	4.57	brandbrand #10
+bar	Extended, local books calm now likely companies. Sometime rich instances improve spanish countries. Crucial flames take further. Rapidly big proposals may not photograph in the opt	12.64	0.55	0.19	namelessbrand #5
+bar	Hours must carry virtually new seats. Polish, happy affairs might get. Originally warm libraries operate real patients. Then soft scie	35.96	28.04	25.23	edu packedu pack #1
+bar	Individual flowers used to give thanks. Particular doubts refer a bit for a directions. Police could	8.31	1.74	1.21	exportiimporto #2
+bar	Low sorts understand. Vegetables must not carry. There legal rates shake so democratic styles. Convenient, single committees might forget	21.35	7.16	5.79	amalgedu pack #1
+bar	Minutes achieve however for a allies. Areas pay apparently alive officers	32.01	3.28	1.34	amalgedu pack #2
+bar	Moments incur pa	32.92	6.14	1.84	exportiimporto #1
+bar	Of course commercial uses look rapidly historical societies. Writers make just high	31.29	3.82	1.33	exportischolar #1
+bar	Old, valuable	23.07	0.23	0.07	namelessnameless #9
+bar	Other things get now. Quite eastern systems should not ask then new days; usual, good friends should work at a proposals. Highly pr	24.90	0.27	0.09	edu packnameless #1
+bar	Pupils change. Frequently nice rates shall not decide future yards. Over upper girls ought to lower in a developments. Formal	36.24	2.19	1.16	edu packexporti #1
+bar	Really foreign workers overcome asleep, young decades. Drugs may tell children; labour, real wages ev	13.88	4.24	2.96	scholarmaxi #9
+bar	Round managers take processes. Primary, particular courses used to hold sacred cases. C	7.23	4.13	2.84	edu packscholar #1
+bar	Simple guests leave british, skilled terms. Kind, little standards must suspect. Combinations may think like, distinguished inches. Artists beat awfully. Ide	27.08	1.68	0.53	edu packedu pack #2
+bar	So damp tests imagine resources. Innocently prime developments shall work small pl	30.83	0.61	0.33	scholarnameless #6
+bar	So much as close reforms would hide at first measures; alone, important contracts lose linguisti	20.14	2.37	1.37	exportinameless #1
+bar	Strange do	15.41	9.47	4.82	exportiexporti #1
+bar	Wide, technical paren	17.06	6.64	3.98	exportiexporti #1
+bar	Wonderful servants must not resolve once physical lives. Later significant an	27.44	0.33	0.22	brandbrand #7
+bar	Years need much. Good interests use too different, junior services. Young items shall not find. Disastrous hands release fast new, alternative applications. American police make in 	33.95	7.68	4.45	exportiamalg #1
+eing	Birds stay foreign, chronic parts. So young cases shall not conclude buildings. About important months may not look; degrees catch just; other societies may not ge	18.42	4.67	2.33	amalgscholar #2
+eing	Central, other hands will agree especially crucial differences	16.71	4.49	3.00	exportischolar #2
+eing	Even single waters make for instance particular hours. Mental rights may cross as just contemporary m	30.54	0.97	0.82	importounivamalg #5
+eing	Flowers cultivate still so-called, available	10.50	3.84	1.22	edu packmaxi #8
+eing	Good, helpful men close. Please difficult lakes should waste very conservative, labour 	11.84	3.13	0.97	edu packbrand #8
+eing	However modern companies ought to make industria	23.09	9.56	3.25	brandcorp #1
+eing	Important, frequent councils explore general, local ideas. Representatives last more. Foreign, sensible pupils pay. Social, american reservations used to get so much	2.86	0.59	0.25	amalgbrand #2
+eing	Increased, special pound	24.05	2.52	1.20	importoedu pack #2
+eing	Laws go shortly british, clear carers. Inner, available aspirations ought to abolish most armed strings. Activities gain then less high banks; never future reactions include so in a powers. Popular, 	30.53	9.69	7.46	edu packmaxi #8
+eing	Main pupils could expel followers. Sometimes severe horses should keep largely earnings. Years put recently permanent inst	19.15	9.17	6.05	maxinameless #2
+eing	More different attempts replace. Changes look shoes. E	25.75	2.47	1.40	amalgexporti #1
+eing	Nations save further new complaints. Perfect things murder different odds. General firms will like also; fatal grounds lie however working sorts. However internal police should design	23.32	8.09	3.31	amalgexporti #1
+eing	Ordinary orders can inspect. New int	34.66	9.58	3.92	exportiedu pack #2
+eing	Other, eager christians live very others. Young, financ	8.49	8.22	5.01	edu packscholar #1
+eing	Parliamentary guests could not convey real chiefs; integrated, full responsibilities take later then important categories. T	28.83	0.41	0.13	edu packamalg #1
+eing	Physical polls melt as eyes. Clear, special sources might invent at once. As immediate things will not	24.71	3.77	1.80	edu packamalg #2
+eing	Specifically honest pp. would ensure wide for a miles. Different families put then western, certain children. Only exciting commitments say f	10.89	0.51	0.26	edu packunivamalg #11
+eing	Students help factors. Seats take matters; likely sources make ridiculous children. Police might say then just natural characters. A	9.32	1.15	0.62	exportischolar #1
+eing	Tired days used to admit for a customs	29.84	5.94	4.93	importoamalg #1
+eing	Wrong, high terms make relatively holidays. Major, relevant theories consider difficult, new markets. Sure, real subjec	27.35	3.29	1.51	amalgscholar #2
+ese		29.58	\N	\N	exportischolar #1
+ese	As sure women fall proposals. Entire, loc	35.61	1.91	1.14	edu packexporti #1
+ese	Backwards royal assets get plans; countries used to swing then. Most strange	28.67	1.84	0.82	exportiedu pack #1
+ese	Civil firms say; prospective technologies used to take there. Easy, high assets enter so practical, structural buildings. Studies woul	26.23	9.09	3.45	maxiunivamalg #6
+ese	Comfortable experiments hit defensive implications. Bad resources would not heal central, national twins. Kind, modern thoughts shall ensure home short	34.17	92.12	71.85	importoedu pack #1
+ese	Companies find financially substances. National, enormous conclusions might object here firms; exactly different jobs should complete; practices encourage really months. Necessary, previous recor	24.08	5.63	3.71	importoamalgamalg #5
+ese	Democrats follow mostly available,	25.69	0.59	0.47	exportibrand #2
+ese	Excellent, real advantages would exist posts. Activities shall continue in a feet. Effects think only confidently local c	25.63	2.41	1.90	amalgunivamalg #11
+ese	Expensive reasons shall not carry hardly ri	19.68	4.59	1.46	scholarbrand #1
+ese	Fresh, industrial vegetables could proceed quite i	29.70	7.16	2.57	scholarunivamalg #8
+ese	Full rules may persuade pregnant cars. Earnings publish worried symptoms. Ready	36.87	5.88	4.99	amalgimporto #1
+ese	Horses last results. There thorough parents sail everywhere into a gua	23.67	3.45	2.55	scholarnameless #5
+ese	Large businessmen might give successful poles; children believe however. Hard, fine companies must not dismiss likely advantages. Now great nations shall not walk to	2.78	3.48	1.07	brandcorp #6
+ese	Modern areas include indeed political children. White, widespread services attend also. Pink boundaries explain early because of a letters. Often assistant men make never pale windows. Then inte	30.08	6.20	4.21	exportiedu pack #2
+ese	Nowhere sure shops ought to constitute by a conditions. Apparent hands shall not fit slightly general men. Oth	26.20	3.59	3.08	amalgscholar #1
+ese	Political, french streets used to introduce just labour 	21.21	1.59	1.38	edu packunivamalg #9
+ese	Publications could not judge; double deputies	35.52	6.30	2.39	edu packamalg #1
+ese	Reasonably direct interests turn. Certainly existing 	12.36	1.86	0.89	importoscholar #1
+ese	Temperatures reflect quite	34.31	0.90	0.45	scholarnameless #3
+ese	Tests will maintain only. Beautifully local banks make still; particular votes protect during a eyes. Contracts must understand primarily. Difficult countries cast in a 	19.06	4.13	2.68	edu packimporto #1
+ese	Totally pure styles would seek charges; values say. Normal, big activi	30.40	1.07	0.84	maxiunivamalg #5
+ese	Years want as a whole. Public eyes shall win against a books. Special minutes intensify stones. Alone, right fingers spring men. Ho	32.52	1.73	0.77	exportibrand #5
+ought	Active abilities depend smoothly by a 	30.54	2.40	1.44	importoexporti #1
+ought	Additional, terrible characters shall examine. Ago lexical conditions get into a weeks. Barely trying results perform still hot men. Great kinds end also committees. Police should live only on the 	18.54	4.46	1.33	corpmaxi #11
+ought	At last political managers would get new, historic workers. Requirements seem loose shadows; activities carry favorite mothers; likely issues stand aside environmental, current funds; below	6.48	1.08	0.43	exportiexporti #1
+ought	Authorities used to leave exactly other co	15.13	2.14	0.72	exporticorp #5
+ought	Costs receive. British teachers evolve mentally only, new words. Good tickets give 	34.78	0.12	0.06	edu packexporti #1
+ought	European, happy homes shall not share. Double calls can cover just in order regular developments; inevitable rooms ought to promise according to a eyes. Normal attempts grow only, complex goods	12.84	8.03	4.57	univnameless #7
+ought	Full-time clothes discharge glad, concerned details. Customs must survive	10.29	8.52	6.73	exportiunivamalg #14
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q66.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q66.out
new file mode 100644
index 0000000000..e7473bf0c8
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q66.out
@@ -0,0 +1,8 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q66 --
+	\N	Fairview	Williamson County	TN	United States	DHL,BARIAN	2001	9597806.95	11121820.57	8670867.91	8994786.04	10887248.09	14187671.36	9732598.41	19798897.07	21007842.34	21495513.67	34795669.17	33122997.94	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	21913594.59	32518476.51	24885662.72	25698343.86	33735910.61	35527031.58	25465193.48	53623238.66	51409986.76	54159173.90	92227043.25	83435390.84
+Bad cards must make.	621234	Fairview	Williamson County	TN	United States	DHL,BARIAN	2001	9506753.46	8008140.33	6116769.63	11973045.15	7756254.92	5352978.49	13733996.10	16418794.37	17212743.32	17042707.41	34304935.61	35324164.21	15.30	12.88	9.83	19.26	12.47	8.61	22.10	26.42	27.70	27.42	55.21	56.85	30534943.77	24481685.94	22178710.81	25695798.18	29954903.78	18084140.05	30805576.13	47156887.22	51158588.86	55759942.80	86253544.16	83451555.63
+Conventional childr	977787	Fairview	Williamson County	TN	United States	DHL,BARIAN	2001	8860645.55	14415813.74	6761497.23	11820654.76	8246260.69	6636877.49	11434492.25	25673812.14	23074206.96	21834581.94	26894900.53	33575091.74	9.05	14.73	6.90	12.08	8.43	6.78	11.69	26.25	23.58	22.32	27.50	34.33	23836085.83	32073313.37	25037904.18	22659895.86	21757401.03	24451608.10	21933001.85	55996703.43	57371880.44	62087214.51	82849910.15	88970319.31
+Doors canno	294242	Fairview	Williamson County	TN	United States	DHL,BARIAN	2001	6355232.31	10198920.36	10246200.97	12209716.50	8566998.28	8806316.81	9789405.60	16466584.88	26443785.61	27016047.80	33660589.67	27462468.62	21.58	34.65	34.81	41.49	29.10	29.91	33.26	55.95	89.86	91.81	114.38	93.32	22645143.09	24487254.60	24925759.42	30503655.27	26558160.29	20976233.52	29895796.09	56002198.38	53488158.53	76287235.46	82483747.59	88088266.69
+Important issues liv	138504	Fairview	Williamson County	TN	United States	DHL,BARIAN	2001	11748784.55	14351305.77	9896470.93	7990874.78	8879247.90	7362383.09	10011144.75	17741201.32	21346976.05	18074978.16	29675125.64	32545325.29	84.81	103.61	71.45	57.69	64.10	53.15	72.27	128.08	154.12	130.49	214.25	234.97	27204167.15	25980378.13	19943398.93	25710421.13	19484481.03	26346611.48	25075158.43	54094778.13	41066732.11	54547058.28	72465962.92	92770328.27
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q67.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q67.out
new file mode 100644
index 0000000000..8e05179bdb
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q67.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q67 --
+\N	\N	\N	\N	\N	\N	\N	\N	3113996.92	2
+\N	\N	\N	\N	\N	\N	\N	\N	1019789218.69	1
+\N		\N	\N	\N	\N	\N	\N	1628997.00	3
+\N			\N	\N	\N	\N	\N	596191.74	4
+\N				\N	\N	\N	\N	102328.59	62
+\N				2000	\N	\N	\N	102328.59	62
+\N			ablepriablebarought	\N	\N	\N	\N	117683.53	38
+\N			ablepriablebarought	2000	\N	\N	\N	117683.53	38
+\N			antiationeinganti	\N	\N	\N	\N	142234.21	15
+\N			antiationeinganti	2000	\N	\N	\N	142234.21	15
+\N			barationableeing	\N	\N	\N	\N	133715.91	25
+\N			barationableeing	2000	\N	\N	\N	133715.91	25
+\N			barationableeing	2000	4	\N	\N	66366.69	100
+\N			eingcallyoughteing	\N	\N	\N	\N	100229.50	67
+\N			eingcallyoughteing	2000	\N	\N	\N	100229.50	67
+\N		brandmaxi #2	\N	\N	\N	\N	\N	102830.03	59
+\N		brandmaxi #2	oughteingought	\N	\N	\N	\N	102830.03	59
+\N		brandmaxi #2	oughteingought	2000	\N	\N	\N	102830.03	59
+\N		corpunivamalg #3	\N	\N	\N	\N	\N	87301.37	80
+\N		corpunivamalg #3	eseantin stationought	\N	\N	\N	\N	87301.37	80
+\N		corpunivamalg #3	eseantin stationought	2000	\N	\N	\N	87301.37	80
+\N		edu packamalg #2	\N	\N	\N	\N	\N	106947.83	48
+\N		edu packamalg #2	oughtn stn stese	\N	\N	\N	\N	106947.83	48
+\N		edu packamalg #2	oughtn stn stese	2000	\N	\N	\N	106947.83	48
+\N		edu packamalgamalg #17	\N	\N	\N	\N	\N	91126.70	77
+\N		edu packamalgamalg #17		\N	\N	\N	\N	91126.70	77
+\N		edu packamalgamalg #17		2000	\N	\N	\N	91126.70	77
+\N		edu packexporti #1	\N	\N	\N	\N	\N	163163.84	6
+\N		edu packexporti #1		\N	\N	\N	\N	163163.84	6
+\N		edu packexporti #1		2000	\N	\N	\N	163163.84	6
+\N		edu packexporti #1		2000	4	\N	\N	68406.85	97
+\N		exportischolar #2	\N	\N	\N	\N	\N	121562.94	31
+\N		exportischolar #2	prieingeseought	\N	\N	\N	\N	121562.94	31
+\N		exportischolar #2	prieingeseought	2000	\N	\N	\N	121562.94	31
+\N		importoedu pack #1	\N	\N	\N	\N	\N	154984.03	9
+\N		importoedu pack #1		\N	\N	\N	\N	154984.03	9
+\N		importoedu pack #1		2000	\N	\N	\N	154984.03	9
+\N		importoedu pack #1		2000	4	\N	\N	73781.23	90
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+\N		namelesscorp #1		\N	\N	\N	\N	148627.34	12
+\N		namelesscorp #1		2000	\N	\N	\N	148627.34	12
+\N		namelesscorp #1		2000	4	\N	\N	67371.60	98
+\N	archery	\N	\N	\N	\N	\N	\N	110088.99	44
+\N	archery	amalgmaxi #6	\N	\N	\N	\N	\N	110088.99	44
+\N	archery	amalgmaxi #6	antioughtn stought	\N	\N	\N	\N	110088.99	44
+\N	archery	amalgmaxi #6	antioughtn stought	2000	\N	\N	\N	110088.99	44
+\N	baseball	\N	\N	\N	\N	\N	\N	93607.10	73
+\N	baseball		\N	\N	\N	\N	\N	93607.10	73
+\N	baseball			\N	\N	\N	\N	93607.10	73
+\N	baseball			2000	\N	\N	\N	93607.10	73
+\N	dresses	\N	\N	\N	\N	\N	\N	138018.80	17
+\N	dresses		\N	\N	\N	\N	\N	138018.80	17
+\N	dresses		antieseoughtcally	\N	\N	\N	\N	138018.80	17
+\N	dresses		antieseoughtcally	2000	\N	\N	\N	138018.80	17
+\N	dresses		antieseoughtcally	2000	4	\N	\N	69138.80	96
+\N	flatware	\N	\N	\N	\N	\N	\N	74808.21	86
+\N	flatware		\N	\N	\N	\N	\N	74808.21	86
+\N	flatware		oughteingationn st	\N	\N	\N	\N	74808.21	86
+\N	flatware		oughteingationn st	2000	\N	\N	\N	74808.21	86
+\N	glassware	\N	\N	\N	\N	\N	\N	98111.30	69
+\N	glassware		\N	\N	\N	\N	\N	98111.30	69
+\N	glassware			\N	\N	\N	\N	98111.30	69
+\N	glassware			2000	\N	\N	\N	98111.30	69
+\N	outdoor	\N	\N	\N	\N	\N	\N	115448.60	40
+\N	outdoor	namelessnameless #3	\N	\N	\N	\N	\N	115448.60	40
+\N	outdoor	namelessnameless #3		\N	\N	\N	\N	115448.60	40
+\N	outdoor	namelessnameless #3		2000	\N	\N	\N	115448.60	40
+\N	pants	\N	\N	\N	\N	\N	\N	135855.95	21
+\N	pants	exportiimporto #2	\N	\N	\N	\N	\N	135855.95	21
+\N	pants	exportiimporto #2	antibarableableought	\N	\N	\N	\N	135855.95	21
+\N	pants	exportiimporto #2	antibarableableought	2000	\N	\N	\N	135855.95	21
+\N	pants	exportiimporto #2	antibarableableought	2000	4	\N	\N	66808.00	99
+\N	scanners	\N	\N	\N	\N	\N	\N	118366.60	34
+\N	scanners	namelessunivamalg #10	\N	\N	\N	\N	\N	118366.60	34
+\N	scanners	namelessunivamalg #10	n stbaresepri	\N	\N	\N	\N	118366.60	34
+\N	scanners	namelessunivamalg #10	n stbaresepri	2000	\N	\N	\N	118366.60	34
+\N	scanners	namelessunivamalg #10	n stbaresepri	2000	4	\N	\N	70357.97	95
+\N	semi-precious	\N	\N	\N	\N	\N	\N	105040.42	51
+\N	semi-precious	amalgbrand #4	\N	\N	\N	\N	\N	105040.42	51
+\N	semi-precious	amalgbrand #4	ationeseoughtanti	\N	\N	\N	\N	105040.42	51
+\N	semi-precious	amalgbrand #4	ationeseoughtanti	2000	\N	\N	\N	105040.42	51
+\N	sports-apparel	\N	\N	\N	\N	\N	\N	104579.05	55
+\N	sports-apparel		\N	\N	\N	\N	\N	104579.05	55
+\N	sports-apparel			\N	\N	\N	\N	104579.05	55
+\N	sports-apparel			2000	\N	\N	\N	104579.05	55
+\N	swimwear	\N	\N	\N	\N	\N	\N	132397.04	27
+\N	swimwear	edu packamalg #2	\N	\N	\N	\N	\N	132397.04	27
+\N	swimwear	edu packamalg #2	antieingoughtcally	\N	\N	\N	\N	132397.04	27
+\N	swimwear	edu packamalg #2	antieingoughtcally	2000	\N	\N	\N	132397.04	27
+\N	tennis	\N	\N	\N	\N	\N	\N	73252.58	91
+\N	tennis		\N	\N	\N	\N	\N	73252.58	91
+\N	tennis		barpribaranti	\N	\N	\N	\N	73252.58	91
+\N	tennis		barpribaranti	2000	\N	\N	\N	73252.58	91
+\N	womens	\N	\N	\N	\N	\N	\N	180234.29	5
+\N	womens		\N	\N	\N	\N	\N	78649.41	83
+\N	womens			\N	\N	\N	\N	78649.41	83
+\N	womens			2000	\N	\N	\N	78649.41	83
+\N	womens	amalgedu pack #2	\N	\N	\N	\N	\N	101584.88	64
+\N	womens	amalgedu pack #2		\N	\N	\N	\N	101584.88	64
+\N	womens	amalgedu pack #2		2000	\N	\N	\N	101584.88	64
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q68.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q68.out
new file mode 100644
index 0000000000..700b85cc76
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q68.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q68 --
+		Unionville	Woodlawn	70	14472.01	383.45	27139.95
+		Midway	Oakwood	11488	13314.57	425.70	30008.53
+	Michael	Oak Grove		15164	21737.79	924.75	43426.33
+	Rene	Caledonia	Stringtown	16638	20732.47	915.66	38600.63
+	John	Parkwood	Edgewood	24159	24087.46	1200.55	51305.29
+	Ana	Deerfield	Jamestown	26329	37277.21	1346.72	65078.34
+	Kent	Wilson	Mountain View	27855	34594.01	1145.23	61220.01
+	Rene	Caledonia	Spring Valley	30016	48048.11	1969.99	88469.89
+		Clinton	Redland	36631	20981.79	1799.58	26288.30
+	Donna	Oak Hill	Sulphur Springs	36870	25603.09	1331.75	53982.97
+		Sunnyside	Newtown	40831	22656.49	1011.26	54581.80
+	Karen	Greenfield	Friendship	40841	15048.50	678.00	45109.61
+		Salem		43618	22846.68	794.43	43131.47
+		Shiloh	Oakland	46266	25416.89	1427.81	45735.58
+	Joshua	Lakeside	Highland Park	46736	29566.29	1020.12	55274.62
+		Newport	Bunker Hill	47193	37645.73	1195.20	72749.99
+		Guilford	Wildwood	49651	14471.24	513.47	32498.08
+		Leon	Five Points	50330	18970.87	926.99	49138.72
+		Salem	Mount Pleasant	50365	26947.72	531.11	39988.78
+		Brownsville	Walnut Grove	53883	23832.80	1031.26	32052.94
+		Shelby	Clifton	54573	25576.31	723.92	42159.67
+	Larry	Oak Hill	Five Points	54597	31630.41	1432.78	55835.87
+		Dallas	Shady Grove	55706	22011.96	313.15	53826.72
+	Tabatha	Harmony	Greenfield	56102	17377.84	420.63	38464.67
+	Shannon	Brownsville	Deerfield	57279	31806.93	1450.73	59987.95
+		Centerville	Providence	62604	6898.02	324.99	30892.81
+		Springfield	Florence	77578	11201.04	573.75	39745.55
+		Fairview	Florence	90439	18472.43	665.54	45369.45
+	Lillian	Fairview	Pleasant Hill	90533	32594.36	1558.26	66144.34
+		White Oak	Florence	92664	13525.33	462.86	28408.35
+		Morris	Sullivan	97140	13728.72	455.24	22377.36
+	Malcolm	Spring Hill	Bridgeport	97846	27789.25	1441.65	70190.53
+	Andrew	Centerville	Plainview	98661	40661.38	973.31	79995.51
+	James	Riverview	Valley View	99563	22376.23	546.85	42749.50
+	Juanita	Waterloo	Georgetown	100509	26660.53	676.41	51566.27
+		Shady Grove	Clifford	103990	20797.70	1019.91	44546.93
+		Florence	Spring Hill	104935	19660.92	1221.31	44977.11
+	David	Florence	Springdale	108460	17730.57	647.69	43637.79
+	Frank	Riverview	Waterloo	109144	18721.91	846.02	29241.20
+		Buena Vista	Griffin	109500	35164.09	1692.37	53432.26
+		Wilson	Jamestown	111773	21025.19	845.83	39863.08
+	Melvin	Woodville	Harmony	115030	15067.30	734.83	31515.14
+	Ricky	Stringtown	Glenwood	120194	22030.71	790.40	54088.54
+	John	Fairview	Lakeside	123374	21712.42	1337.39	31640.02
+	Ralph	Valley View	Harmony	125234	17152.89	603.81	43572.89
+			Bunker Hill	133136	15951.17	341.13	30764.94
+	Bernadette	Ashland	Pleasant Grove	134217	6757.46	306.65	17709.08
+	Joseph	Springdale	Lakeside	134343	17381.47	748.31	37418.87
+	Lacey	Jamestown	Riverside	134462	10125.84	293.43	26180.45
+	Warren	Bunker Hill	Greenville	134828	10481.74	350.80	19160.46
+	Gertrude	Greenwood	Macedonia	137410	33678.21	1866.59	47276.52
+		Galena	Sulphur Springs	137765	12788.84	545.88	35276.70
+		Forest Hills	Argyle	139387	14190.66	566.22	26171.36
+	James	Shiloh	Wesley	142680	28622.37	1046.33	61645.20
+	Susan	Green Acres	Liberty	143399	13352.11	880.61	30549.47
+	Willie	Freeport	Wyoming	147738	15265.05	608.18	43336.34
+		Walnut Grove	Willow	147740	24550.37	1403.66	47888.99
+	Jack	Oakwood	Liberty	149758	18006.16	299.45	37000.83
+		Pleasant Hill	Waterloo	156554	20178.41	1143.87	34449.89
+		Five Points	Tracy	159202	32371.98	1162.21	65641.10
+		Glendale	Mount Pleasant	159389	19529.56	568.73	42661.40
+	Victor	Providence	Antioch	161524	14173.12	860.07	26079.25
+		Pleasant Grove	Newtown	164893	15334.29	612.95	39959.97
+		Centerville		171076	6706.91	269.11	19594.55
+	Raymond	Springfield	Greenfield	174183	26633.39	1132.88	66071.66
+		Mount Zion	Greenwood	174923	17133.91	313.95	51727.01
+		Kingston	Wildwood	176614	38949.49	2260.34	62962.63
+		Springfield	Jackson	176898	22035.46	785.41	36552.00
+	George	Pleasant Grove	Shiloh	179953	18237.84	607.57	44135.38
+		Newtown	Union Hill	186823	18161.92	709.16	27119.67
+	Antone	Antioch	Forest Hills	192973	14507.02	841.86	34973.08
+	Violet	Woodlawn	Woodville	193534	17536.20	1067.01	30308.64
+	Emiko	Liberty	Oakdale	193771	9730.58	249.23	22985.99
+		Mountain View	Deerfield	194436	16962.72	773.97	33070.54
+		Georgetown	Forest Hills	195266	11924.71	382.35	28208.78
+	Roy	Georgetown	Riverside	200033	35666.28	644.10	62654.54
+	Barbara	Westgate	Crossroads	202167	17296.28	1017.79	30562.26
+	Diane	Five Points	Mount Zion	203347	26281.72	533.37	42267.33
+	Andrew	Shiloh	Mount Zion	205712	18320.09	575.85	32773.59
+		Mount Pleasant	Greenville	206301	20248.08	682.37	57143.50
+		Waterloo	Spring Hill	206784	21355.86	1366.98	37107.65
+		Littleton	Walnut Grove	209023	22998.20	640.55	41076.55
+	Norman	Jamestown	Farmington	218063	37475.56	1480.70	61106.57
+		Hillcrest	Valley View	218569	37536.35	1322.01	54952.26
+	Douglas	Crossroads	Sunnyside	218776	22822.92	884.66	42559.53
+		Summit	Concord	224470	17167.29	893.49	36546.18
+	Joshua	Five Points	Hopewell	227114	19755.36	674.30	28406.59
+		Edgewood	Belmont	230383	14998.93	844.15	26544.25
+		Franklin	Floyd	230648	16319.23	871.53	38604.51
+	Joseph	Lakewood	Mount Olive	231104	34934.39	1256.89	55986.84
+	Marti	Greenwood	Providence	231861	18475.92	694.12	33988.12
+		Deerfield	Lakeside	232906	18068.60	650.65	43837.36
+		Fisher		234655	20093.46	279.93	39719.79
+		Lakeside	Jackson	235093	15888.52	457.78	39640.17
+Aaron	Guy	Lakeside	Mount Pleasant	210815	24704.66	678.88	45673.18
+Abbott	Joseph	Greenfield	Midway	62751	37795.50	1086.40	53385.17
+Abbott	Harriet	Wesley	Forest Hills	84405	23666.36	1274.86	45689.64
+Abney	Louis	Gladstone	Green Acres	140967	20698.82	491.86	48086.21
+Acevedo	David	Clinton	Shady Grove	131505	22075.16	772.60	46961.99
+Acosta	Johnnie	Centerville	Enterprise	69183	21688.97	1059.74	34387.30
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q69.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q69.out
new file mode 100644
index 0000000000..340fcba2f4
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q69.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q69 --
+F	D	2 yr Degree	1	2500	1	Low Risk	1
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q70.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q70.out
new file mode 100644
index 0000000000..83acfd2ba3
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q70.out
@@ -0,0 +1,6 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q70 --
+-440986113.22	\N	\N	2	1
+-440986113.22	TN	\N	1	1
+-440986113.22	TN	Williamson County	0	1
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q71.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q71.out
new file mode 100644
index 0000000000..5223bf4357
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q71.out
@@ -0,0 +1,1021 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q71 --
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+3002001	importoexporti #1	9	59	6964.25
+6008007	namelesscorp #7	18	47	6953.36
+7001005	amalgbrand #5	8	12	6940.96
+1002002	importoamalg #2	18	51	6924.69
+5001001	amalgscholar #1	19	57	6841.12
+6015006	scholarbrand #6	18	23	6755.76
+5002002	importoscholar #2	19	31	6745.90
+2004002	edu packimporto #2	17	2	6744.98
+5003001	exportischolar #1	9	24	6694.80
+10002012	importounivamalg #12	18	16	6634.95
+5004001	edu packscholar #1	8	19	6527.50
+5003001	exportischolar #1	9	53	6525.33
+1004002	edu packamalg #2	17	32	6524.70
+1002001	importoamalg #1	17	22	6511.08
+3002002	importoexporti #2	19	13	6487.20
+10002012	importounivamalg #12	18	3	6471.90
+3003001	exportiexporti #1	17	9	6450.60
+4004002	edu packedu pack #2	17	3	6447.42
+1002001	importoamalg #1	6	6	6334.24
+10009015	maxiunivamalg #15	8	32	6322.29
+10012004	importoamalgamalg #4	6	49	6234.24
+10012011	importoamalgamalg #11	19	27	6218.47
+9008002	namelessmaxi #2	17	41	6207.45
+3002001	importoexporti #1	18	3	6203.20
+7004007	edu packbrand #7	9	23	6173.10
+1004002	edu packamalg #2	17	2	6118.65
+5001002	amalgscholar #2	18	15	6118.20
+6012005	importobrand #5	17	30	6063.57
+1003002	exportiamalg #2	18	52	6013.26
+4003001	exportiedu pack #1	9	54	5994.50
+3003001	exportiexporti #1	19	38	5990.76
+7010003	univnameless #3	19	4	5952.32
+3002001	importoexporti #1	18	50	5950.48
+9012003	importounivamalg #3	8	30	5922.09
+1004001	edu packamalg #1	17	57	5860.74
+8013009	exportimaxi #9	9	53	5808.00
+10010013	univamalgamalg #13	7	11	5799.04
+10003004	exportiunivamalg #4	18	18	5692.22
+8002009	importonameless #9	19	33	5633.00
+1003001	exportiamalg #1	7	57	5617.50
+10010013	univamalgamalg #13	17	7	5590.62
+5003001	exportischolar #1	7	44	5589.90
+6006007	corpcorp #7	6	1	5578.24
+9008002	namelessmaxi #2	17	33	5547.78
+10003016	exportiunivamalg #16	17	55	5519.20
+5002001	importoscholar #1	18	2	5471.54
+10012017	importoamalgamalg #17	18	54	5415.90
+10012011	importoamalgamalg #11	18	17	5384.79
+1004002	edu packamalg #2	9	53	5382.72
+7016001	corpnameless #1	17	32	5382.09
+9008005	namelessmaxi #5	6	9	5382.09
+2001001	amalgimporto #1	6	40	5295.40
+3002001	importoexporti #1	6	40	5245.80
+1002002	importoamalg #2	18	15	5240.76
+3004001	edu packexporti #1	9	36	5218.56
+5003001	exportischolar #1	17	14	5214.72
+3003002	exportiexporti #2	19	5	5201.25
+2004002	edu packimporto #2	9	41	5165.58
+9013009	exportiunivamalg #9	7	31	5155.29
+2003001	exportiimporto #1	18	28	5107.41
+3003001	exportiexporti #1	17	56	5104.62
+6015006	scholarbrand #6	19	58	5080.60
+3003001	exportiexporti #1	8	32	5008.64
+10004001	edu packunivamalg #1	9	20	5002.40
+8013007	exportimaxi #7	19	3	4989.95
+8016004	corpmaxi #4	19	8	4979.25
+6003008	exporticorp #8	18	38	4955.40
+3002002	importoexporti #2	8	51	4938.16
+8014005	edu packmaxi #5	19	38	4893.48
+5003001	exportischolar #1	18	1	4889.22
+6003008	exporticorp #8	9	18	4794.00
+5002001	importoscholar #1	19	9	4787.43
+4003001	exportiedu pack #1	9	51	4781.14
+9015009	scholarunivamalg #9	8	52	4774.34
+5002002	importoscholar #2	6	3	4774.25
+1001002	amalgamalg #2	19	31	4758.75
+5002002	importoscholar #2	19	15	4754.40
+2003001	exportiimporto #1	9	32	4723.95
+3003001	exportiexporti #1	19	40	4711.07
+10003004	exportiunivamalg #4	19	43	4701.06
+2001002	amalgimporto #2	19	9	4681.05
+7014001	edu packnameless #1	6	35	4646.40
+5001001	amalgscholar #1	17	56	4638.84
+6010005	univbrand #5	19	23	4606.83
+1004002	edu packamalg #2	7	0	4599.14
+6016003	corpbrand #3	17	33	4592.04
+4001001	amalgedu pack #1	17	8	4586.22
+9012005	importounivamalg #5	19	59	4581.56
+3002001	importoexporti #1	17	5	4561.20
+10012011	importoamalgamalg #11	18	29	4531.45
+6008005	namelesscorp #5	18	27	4498.44
+1003001	exportiamalg #1	19	4	4483.98
+5003001	exportischolar #1	18	31	4452.80
+3002002	importoexporti #2	18	52	4436.80
+1003002	exportiamalg #2	9	38	4428.98
+1004002	edu packamalg #2	9	55	4427.52
+5003001	exportischolar #1	19	2	4412.78
+1003002	exportiamalg #2	18	53	4374.72
+6005001	scholarcorp #1	17	55	4367.72
+9012008	importounivamalg #8	18	11	4354.35
+5002001	importoscholar #1	18	32	4324.52
+7013007	exportinameless #7	18	47	4323.55
+9013009	exportiunivamalg #9	17	42	4321.92
+4004001	edu packedu pack #1	8	22	4316.62
+8004003	edu packnameless #3	17	23	4282.17
+2004002	edu packimporto #2	7	56	4267.90
+1003002	exportiamalg #2	9	49	4255.80
+7013007	exportinameless #7	19	42	4252.78
+9015011	scholarunivamalg #11	8	55	4235.00
+1002002	importoamalg #2	17	0	4183.34
+6010005	univbrand #5	9	27	4172.35
+6007003	brandcorp #3	19	33	4146.75
+2004001	edu packimporto #1	19	33	4138.56
+6005001	scholarcorp #1	19	55	4137.88
+6016003	corpbrand #3	9	41	4100.88
+9014011	edu packunivamalg #11	17	50	4100.40
+5003001	exportischolar #1	18	17	4099.20
+5003001	exportischolar #1	9	28	4064.76
+1004002	edu packamalg #2	17	16	4061.68
+10012017	importoamalgamalg #17	8	53	4045.00
+4004001	edu packedu pack #1	17	15	4028.96
+6002005	importocorp #5	17	22	4011.30
+4003001	exportiedu pack #1	9	42	3952.59
+7004007	edu packbrand #7	7	19	3943.82
+10002012	importounivamalg #12	19	19	3925.48
+10009015	maxiunivamalg #15	7	51	3912.99
+4001001	amalgedu pack #1	17	55	3890.08
+10002012	importounivamalg #12	18	10	3870.00
+10004001	edu packunivamalg #1	18	13	3864.00
+5004002	edu packscholar #2	18	50	3863.88
+6008002	namelesscorp #2	18	38	3841.04
+1001001	amalgamalg #1	19	44	3818.40
+10002012	importounivamalg #12	17	51	3766.14
+2004001	edu packimporto #1	18	4	3757.34
+10003004	exportiunivamalg #4	7	34	3756.20
+9012008	importounivamalg #8	18	56	3735.84
+5001001	amalgscholar #1	19	12	3735.76
+7015007	scholarnameless #7	17	55	3734.28
+2001002	amalgimporto #2	8	51	3725.90
+5002002	importoscholar #2	17	38	3709.44
+7016007	corpnameless #7	19	53	3701.95
+8002009	importonameless #9	9	18	3686.90
+8014005	edu packmaxi #5	18	43	3649.00
+9008002	namelessmaxi #2	17	52	3635.28
+5001001	amalgscholar #1	19	19	3617.97
+4003001	exportiedu pack #1	17	43	3598.98
+6009003	maxicorp #3	17	6	3596.58
+7012003	importonameless #3	9	29	3573.75
+9012003	importounivamalg #3	17	2	3568.96
+1001002	amalgamalg #2	19	59	3542.94
+1001002	amalgamalg #2	18	37	3522.98
+8011009	amalgmaxi #9	19	35	3485.72
+1003002	exportiamalg #2	19	58	3423.80
+1001002	amalgamalg #2	18	25	3408.48
+4004002	edu packedu pack #2	17	2	3400.05
+6008007	namelesscorp #7	18	16	3395.62
+5002002	importoscholar #2	6	23	3383.10
+1002002	importoamalg #2	9	48	3374.00
+4003001	exportiedu pack #1	8	33	3358.72
+5001001	amalgscholar #1	18	27	3326.04
+3002002	importoexporti #2	17	18	3304.55
+7010004	univnameless #4	18	7	3301.28
+9016003	corpunivamalg #3	8	42	3279.18
+6016003	corpbrand #3	9	9	3262.08
+7015007	scholarnameless #7	9	48	3249.72
+8004003	edu packnameless #3	18	44	3232.48
+5003002	exportischolar #2	9	47	3222.66
+1002002	importoamalg #2	8	37	3220.80
+3004001	edu packexporti #1	7	56	3218.22
+3001001	amalgexporti #1	17	10	3217.50
+7010009	univnameless #9	18	26	3215.11
+1002001	importoamalg #1	17	46	3195.24
+2004002	edu packimporto #2	18	46	3194.31
+5001001	amalgscholar #1	17	54	3180.02
+7008009	namelessbrand #9	17	52	3146.96
+2004002	edu packimporto #2	17	46	3144.40
+3004001	edu packexporti #1	19	14	3127.20
+7010004	univnameless #4	9	16	3126.90
+3001001	amalgexporti #1	8	38	3125.32
+1003001	exportiamalg #1	19	10	3110.49
+6012008	importobrand #8	19	13	3101.32
+1003002	exportiamalg #2	8	56	3097.12
+6005001	scholarcorp #1	8	14	3096.90
+6016003	corpbrand #3	19	31	3094.50
+7010004	univnameless #4	19	4	3093.35
+10002012	importounivamalg #12	18	0	3063.20
+2004001	edu packimporto #1	19	4	3051.40
+8014005	edu packmaxi #5	17	40	3049.76
+1001001	amalgamalg #1	18	49	3045.12
+3004001	edu packexporti #1	7	50	3029.00
+10012017	importoamalgamalg #17	18	26	3027.72
+1003001	exportiamalg #1	8	35	3007.26
+8005005	scholarnameless #5	6	50	2972.97
+1003002	exportiamalg #2	8	25	2957.35
+4001001	amalgedu pack #1	9	12	2930.95
+1002002	importoamalg #2	9	9	2926.95
+6008005	namelesscorp #5	9	1	2902.58
+3002002	importoexporti #2	17	15	2902.20
+2001002	amalgimporto #2	19	47	2894.46
+1003001	exportiamalg #1	9	58	2871.44
+9012005	importounivamalg #5	18	34	2864.88
+3003002	exportiexporti #2	18	8	2844.00
+10013013	exportiamalgamalg #13	8	0	2831.12
+5002001	importoscholar #1	17	38	2822.47
+7016007	corpnameless #7	17	56	2817.48
+9015011	scholarunivamalg #11	8	2	2811.48
+1001001	amalgamalg #1	9	18	2793.44
+4004001	edu packedu pack #1	9	54	2787.20
+1003001	exportiamalg #1	7	23	2784.84
+1003002	exportiamalg #2	18	21	2783.00
+1003001	exportiamalg #1	17	37	2779.50
+2004002	edu packimporto #2	19	26	2776.36
+6009003	maxicorp #3	19	38	2763.09
+1003001	exportiamalg #1	17	41	2751.84
+2001002	amalgimporto #2	18	24	2749.95
+10015013	scholaramalgamalg #13	8	25	2746.86
+3002001	importoexporti #1	17	13	2741.54
+4001001	amalgedu pack #1	9	3	2738.34
+9012003	importounivamalg #3	9	18	2732.40
+5001001	amalgscholar #1	17	43	2731.37
+2001002	amalgimporto #2	9	36	2715.18
+3003001	exportiexporti #1	19	25	2713.30
+5001001	amalgscholar #1	17	52	2707.54
+3002001	importoexporti #1	18	20	2694.50
+6008002	namelesscorp #2	18	48	2683.20
+1003002	exportiamalg #2	17	59	2670.50
+1002002	importoamalg #2	9	36	2668.77
+1003001	exportiamalg #1	9	26	2633.15
+4003001	exportiedu pack #1	9	38	2631.20
+9015011	scholarunivamalg #11	18	16	2621.71
+7010009	univnameless #9	18	48	2609.88
+8005005	scholarnameless #5	19	55	2606.45
+10012011	importoamalgamalg #11	17	9	2589.95
+1002001	importoamalg #1	19	21	2579.48
+4002001	importoedu pack #1	18	51	2567.40
+5003001	exportischolar #1	8	50	2555.28
+1004002	edu packamalg #2	18	24	2551.16
+2003001	exportiimporto #1	19	58	2528.40
+6005001	scholarcorp #1	17	45	2522.52
+6007003	brandcorp #3	17	49	2522.15
+1002001	importoamalg #1	19	14	2513.30
+10010013	univamalgamalg #13	8	30	2513.25
+4001001	amalgedu pack #1	19	9	2512.00
+7010004	univnameless #4	17	44	2509.78
+8003010	exportinameless #10	18	27	2507.44
+6011008	amalgbrand #8	18	5	2505.00
+7010004	univnameless #4	8	21	2502.36
+6012008	importobrand #8	8	33	2495.65
+8004003	edu packnameless #3	18	52	2495.22
+1001002	amalgamalg #2	18	11	2491.16
+7010003	univnameless #3	7	5	2485.08
+4004002	edu packedu pack #2	18	23	2483.01
+8016004	corpmaxi #4	18	43	2482.62
+10004001	edu packunivamalg #1	8	39	2472.93
+2003002	exportiimporto #2	9	41	2471.90
+6011001	amalgbrand #1	7	21	2458.17
+8011009	amalgmaxi #9	19	5	2457.99
+6002005	importocorp #5	19	0	2454.03
+1002002	importoamalg #2	19	57	2451.06
+5001002	amalgscholar #2	19	42	2444.80
+9014011	edu packunivamalg #11	17	44	2436.40
+10003004	exportiunivamalg #4	19	46	2423.18
+6016003	corpbrand #3	19	19	2421.65
+8004009	edu packnameless #9	19	44	2421.51
+2002001	importoimporto #1	8	8	2420.34
+6012005	importobrand #5	7	20	2412.30
+3003001	exportiexporti #1	7	29	2408.70
+3002001	importoexporti #1	18	21	2403.96
+3002002	importoexporti #2	8	58	2399.13
+2001002	amalgimporto #2	17	12	2396.61
+5003002	exportischolar #2	17	37	2388.24
+4004001	edu packedu pack #1	17	5	2386.20
+5003002	exportischolar #2	9	29	2378.69
+3004001	edu packexporti #1	17	46	2375.45
+9014011	edu packunivamalg #11	19	18	2363.04
+1003001	exportiamalg #1	8	5	2360.81
+1003001	exportiamalg #1	19	19	2355.12
+6002005	importocorp #5	8	27	2351.04
+2002001	importoimporto #1	19	47	2338.56
+6007003	brandcorp #3	9	4	2335.10
+8004003	edu packnameless #3	9	44	2314.40
+5003001	exportischolar #1	19	58	2294.62
+8002009	importonameless #9	18	12	2266.60
+3004001	edu packexporti #1	19	9	2261.52
+7010003	univnameless #3	18	35	2234.40
+4004001	edu packedu pack #1	9	25	2215.21
+3003001	exportiexporti #1	8	9	2206.47
+1003002	exportiamalg #2	17	41	2197.60
+6008007	namelesscorp #7	18	0	2190.24
+3002001	importoexporti #1	17	51	2181.96
+8013007	exportimaxi #7	7	11	2180.76
+6005001	scholarcorp #1	18	43	2174.48
+6006007	corpcorp #7	7	24	2158.40
+6002003	importocorp #3	19	26	2152.17
+1001001	amalgamalg #1	19	53	2149.70
+5003001	exportischolar #1	8	59	2144.73
+6005001	scholarcorp #1	9	25	2143.10
+7008009	namelessbrand #9	19	39	2132.41
+3003001	exportiexporti #1	19	53	2118.88
+9015009	scholarunivamalg #9	19	3	2114.64
+10013013	exportiamalgamalg #13	7	33	2103.93
+8003010	exportinameless #10	17	28	2084.72
+8003010	exportinameless #10	17	42	2082.86
+8002009	importonameless #9	9	37	2079.84
+4001001	amalgedu pack #1	19	43	2067.52
+5004002	edu packscholar #2	19	2	2061.30
+5004002	edu packscholar #2	18	7	2049.67
+10004001	edu packunivamalg #1	18	49	2031.20
+1003001	exportiamalg #1	18	21	2028.31
+4001002	amalgedu pack #2	19	19	2018.40
+5002002	importoscholar #2	19	18	2016.96
+4001002	amalgedu pack #2	6	3	2012.80
+6011005	amalgbrand #5	9	53	1997.52
+6011008	amalgbrand #8	8	28	1995.60
+6007003	brandcorp #3	17	33	1995.20
+3003001	exportiexporti #1	9	22	1982.73
+5002002	importoscholar #2	17	49	1980.88
+2001002	amalgimporto #2	17	27	1955.20
+7007004	brandbrand #4	9	31	1954.11
+2001002	amalgimporto #2	17	11	1951.95
+2004001	edu packimporto #1	19	50	1934.46
+4004001	edu packedu pack #1	9	11	1930.50
+7010003	univnameless #3	19	20	1926.24
+1004002	edu packamalg #2	8	13	1922.68
+9013009	exportiunivamalg #9	19	55	1906.92
+8004003	edu packnameless #3	19	58	1905.78
+6008007	namelesscorp #7	17	2	1905.12
+1002001	importoamalg #1	9	28	1904.10
+1002002	importoamalg #2	9	42	1893.54
+4001002	amalgedu pack #2	17	55	1880.97
+1004001	edu packamalg #1	19	42	1878.76
+1001001	amalgamalg #1	17	19	1877.46
+2003002	exportiimporto #2	18	9	1874.40
+9012008	importounivamalg #8	18	22	1873.02
+1002002	importoamalg #2	18	42	1852.20
+6002003	importocorp #3	17	39	1840.98
+5002002	importoscholar #2	18	14	1817.00
+1001001	amalgamalg #1	17	59	1813.11
+7012003	importonameless #3	18	18	1807.85
+7016001	corpnameless #1	19	5	1806.68
+8005005	scholarnameless #5	17	31	1800.48
+5002001	importoscholar #1	6	7	1789.20
+9015009	scholarunivamalg #9	6	44	1788.93
+3002001	importoexporti #1	8	52	1770.00
+3002002	importoexporti #2	6	41	1760.58
+1001001	amalgamalg #1	9	14	1745.24
+1004002	edu packamalg #2	18	32	1742.52
+9013009	exportiunivamalg #9	9	36	1737.57
+10015004	scholaramalgamalg #4	9	2	1736.72
+7010003	univnameless #3	7	58	1735.12
+6016003	corpbrand #3	8	54	1733.08
+7008009	namelessbrand #9	19	27	1726.56
+5002001	importoscholar #1	8	24	1725.50
+9013009	exportiunivamalg #9	18	43	1722.16
+2002001	importoimporto #1	9	43	1715.64
+5003001	exportischolar #1	17	32	1714.80
+5001001	amalgscholar #1	18	35	1713.96
+7004007	edu packbrand #7	17	57	1713.28
+4001001	amalgedu pack #1	19	13	1708.26
+6007003	brandcorp #3	17	51	1707.48
+4001002	amalgedu pack #2	19	51	1706.46
+1003001	exportiamalg #1	18	3	1704.75
+10010013	univamalgamalg #13	18	51	1677.20
+2004002	edu packimporto #2	17	59	1676.84
+2001002	amalgimporto #2	18	21	1659.84
+5001002	amalgscholar #2	18	35	1654.44
+2002001	importoimporto #1	17	4	1649.28
+5004001	edu packscholar #1	6	36	1646.50
+3002001	importoexporti #1	17	14	1644.96
+4004001	edu packedu pack #1	17	18	1644.00
+4001001	amalgedu pack #1	17	28	1643.76
+5001001	amalgscholar #1	19	21	1643.22
+4001001	amalgedu pack #1	6	45	1639.44
+1004001	edu packamalg #1	8	16	1629.81
+1001002	amalgamalg #2	9	48	1624.59
+9008005	namelessmaxi #5	17	4	1621.62
+1002002	importoamalg #2	9	56	1600.82
+9013009	exportiunivamalg #9	18	51	1590.00
+10012017	importoamalgamalg #17	17	16	1565.38
+5002001	importoscholar #1	19	55	1563.38
+10015013	scholaramalgamalg #13	19	51	1553.20
+3004001	edu packexporti #1	9	31	1541.43
+5003002	exportischolar #2	17	2	1540.54
+8013007	exportimaxi #7	18	51	1537.90
+5004002	edu packscholar #2	19	24	1514.74
+4003001	exportiedu pack #1	8	3	1514.43
+2001002	amalgimporto #2	18	48	1511.30
+1001002	amalgamalg #2	17	55	1505.25
+2004001	edu packimporto #1	19	27	1490.13
+4001001	amalgedu pack #1	17	15	1488.84
+6005001	scholarcorp #1	17	14	1488.52
+9015011	scholarunivamalg #11	18	55	1487.52
+5003001	exportischolar #1	9	26	1482.30
+5004002	edu packscholar #2	18	47	1481.61
+10009015	maxiunivamalg #15	19	55	1476.75
+2004002	edu packimporto #2	9	3	1475.84
+7016007	corpnameless #7	17	42	1470.52
+5003001	exportischolar #1	17	59	1466.25
+5004002	edu packscholar #2	18	35	1462.00
+5003001	exportischolar #1	18	10	1461.98
+1003001	exportiamalg #1	19	50	1455.83
+5003001	exportischolar #1	8	25	1446.36
+1004001	edu packamalg #1	19	26	1439.04
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+5002001	importoscholar #1	19	33	1434.24
+3004001	edu packexporti #1	18	49	1433.88
+4001002	amalgedu pack #2	7	40	1431.54
+4004001	edu packedu pack #1	17	37	1430.37
+1003002	exportiamalg #2	7	40	1418.18
+1002002	importoamalg #2	17	44	1415.25
+2001002	amalgimporto #2	7	8	1414.14
+5002002	importoscholar #2	17	30	1404.00
+5003001	exportischolar #1	19	10	1403.22
+7012003	importonameless #3	9	22	1399.96
+3003001	exportiexporti #1	18	33	1386.35
+3002001	importoexporti #1	17	40	1379.04
+9014011	edu packunivamalg #11	17	3	1364.52
+5003001	exportischolar #1	9	46	1361.57
+5001002	amalgscholar #2	19	51	1360.00
+2004002	edu packimporto #2	9	28	1353.15
+9015009	scholarunivamalg #9	18	9	1352.61
+5002002	importoscholar #2	17	52	1345.02
+1002002	importoamalg #2	18	27	1343.93
+6011008	amalgbrand #8	9	39	1341.93
+1004002	edu packamalg #2	17	11	1340.64
+5003001	exportischolar #1	17	11	1328.00
+6002003	importocorp #3	17	20	1327.58
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+2003001	exportiimporto #1	9	23	1310.16
+3002002	importoexporti #2	17	45	1301.90
+3003001	exportiexporti #1	8	19	1301.37
+6011008	amalgbrand #8	9	33	1278.35
+10003004	exportiunivamalg #4	9	23	1272.43
+3004001	edu packexporti #1	18	21	1270.66
+2001002	amalgimporto #2	17	22	1269.07
+4004002	edu packedu pack #2	19	7	1264.56
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+10015013	scholaramalgamalg #13	9	34	1257.30
+1001001	amalgamalg #1	17	56	1250.08
+3003002	exportiexporti #2	18	26	1248.48
+9012005	importounivamalg #5	7	0	1230.20
+9015009	scholarunivamalg #9	18	40	1222.64
+3003002	exportiexporti #2	9	42	1217.28
+2001002	amalgimporto #2	7	46	1213.44
+10004001	edu packunivamalg #1	18	58	1212.96
+3001001	amalgexporti #1	9	41	1211.50
+6006007	corpcorp #7	18	59	1210.32
+5003001	exportischolar #1	18	14	1210.20
+5001001	amalgscholar #1	8	31	1205.50
+9016003	corpunivamalg #3	17	53	1200.00
+10013015	exportiamalgamalg #15	17	14	1192.50
+1002002	importoamalg #2	19	16	1182.37
+1002002	importoamalg #2	19	42	1178.88
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+5002002	importoscholar #2	18	40	1159.40
+2001002	amalgimporto #2	9	3	1157.20
+5003002	exportischolar #2	19	6	1150.92
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+10009015	maxiunivamalg #15	18	2	1150.73
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+3003001	exportiexporti #1	9	14	1139.60
+2001001	amalgimporto #1	17	28	1135.06
+4003001	exportiedu pack #1	6	50	1129.20
+8016004	corpmaxi #4	17	32	1127.61
+9015009	scholarunivamalg #9	18	18	1124.16
+5002001	importoscholar #1	8	48	1123.47
+8003010	exportinameless #10	9	0	1123.08
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+1001002	amalgamalg #2	9	46	1114.40
+10012017	importoamalgamalg #17	17	7	1107.54
+10009015	maxiunivamalg #15	17	29	1104.61
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+3002001	importoexporti #1	18	39	1094.10
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+9016003	corpunivamalg #3	19	8	1079.54
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+6008007	namelesscorp #7	19	54	1058.93
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+10013015	exportiamalgamalg #15	8	30	1057.80
+6006007	corpcorp #7	19	54	1055.43
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+7008009	namelessbrand #9	19	45	1037.51
+2002001	importoimporto #1	7	32	1023.75
+7012010	importonameless #10	7	19	1016.80
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+7016007	corpnameless #7	18	45	992.74
+6005001	scholarcorp #1	19	16	991.98
+5002002	importoscholar #2	18	27	986.24
+8002009	importonameless #9	9	23	983.40
+1002001	importoamalg #1	19	26	977.60
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+8011009	amalgmaxi #9	19	42	951.60
+4004002	edu packedu pack #2	9	34	951.00
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+2002001	importoimporto #1	9	20	943.56
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+5001001	amalgscholar #1	19	54	920.16
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+1002001	importoamalg #1	19	40	901.60
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+9014011	edu packunivamalg #11	8	40	896.49
+7016007	corpnameless #7	19	16	892.64
+4001002	amalgedu pack #2	17	14	886.48
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+8004009	edu packnameless #9	17	8	829.76
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+9012005	importounivamalg #5	17	45	821.48
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+7016001	corpnameless #1	19	50	817.11
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+6008007	namelesscorp #7	17	20	810.46
+7016007	corpnameless #7	9	54	810.24
+9012008	importounivamalg #8	9	43	803.64
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+9008005	namelessmaxi #5	9	52	756.42
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+8014005	edu packmaxi #5	17	11	695.64
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+9008002	namelessmaxi #2	17	4	660.04
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+10010013	univamalgamalg #13	18	32	630.00
+1002002	importoamalg #2	19	21	628.67
+10012017	importoamalgamalg #17	17	19	625.56
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+6005001	scholarcorp #1	9	9	597.30
+10012017	importoamalgamalg #17	19	11	595.65
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+6007003	brandcorp #3	17	26	588.80
+1002001	importoamalg #1	19	46	583.47
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+10015013	scholaramalgamalg #13	18	20	574.24
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+9008005	namelessmaxi #5	17	42	567.64
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+10012017	importoamalgamalg #17	8	4	558.69
+9015011	scholarunivamalg #11	19	54	555.36
+1002001	importoamalg #1	19	20	554.88
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+7008009	namelessbrand #9	17	29	514.25
+9012005	importounivamalg #5	8	34	504.60
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+6008002	namelesscorp #2	8	59	471.68
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+8016004	corpmaxi #4	7	53	470.55
+4001001	amalgedu pack #1	17	26	469.58
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+9008002	namelessmaxi #2	9	51	458.82
+6009003	maxicorp #3	17	44	457.74
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+10015013	scholaramalgamalg #13	8	32	438.48
+4003001	exportiedu pack #1	9	31	424.26
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+1002001	importoamalg #1	8	24	418.92
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+2003002	exportiimporto #2	18	18	414.12
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+5001001	amalgscholar #1	18	40	405.15
+10009015	maxiunivamalg #15	9	58	402.38
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+1001002	amalgamalg #2	8	38	388.35
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+6006007	corpcorp #7	18	53	375.36
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+8014005	edu packmaxi #5	17	2	352.36
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+8013007	exportimaxi #7	17	49	318.20
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+4001001	amalgedu pack #1	9	48	211.32
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q72.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q72.out
new file mode 100644
index 0000000000..8d050a5d2c
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q72.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q72 --
+Best possible ages tell together new, st	Conventional childr	5213	0	2	2
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+Departments make once again police. Very acceptable results call still extended, known ends; relationships shoot strangely. Acids shall discharge in order ethnic, ric		5168	0	2	2
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+Provisions go too. Sad others contain italian branches. Keys k	Doors canno	5213	0	2	2
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+Able, long mammals can want new, serious years. Questions would not cope again mainly unable contributions. Less responsible shelves lose records; leading, similar 	Important issues liv	5170	0	1	1
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+About international concentrations could avoid then alone apparent activities; inadequate, mediterranean days get eve	Important issues liv	5215	0	1	1
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+About working feelings could produce only types. Electoral, new visitors will not make more afraid, large tr		5197	0	1	1
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+Actively different proceedings light yet so similar houses. Good circumstances shall not take only levels. Then moral pounds will clean very only national organisations. 		5183	0	1	1
+Activities say. Right lips resort current techniques. Regional, possible daughters might not present changes; students can notice ridiculous, l	Conventional childr	5192	0	1	1
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+Acute, important performances afford. New, nuclear men used to assess again small results.		5187	0	1	1
+Added, similar grounds spend also concrete terms. Fellow, mass	Important issues liv	5206	0	1	1
+Additional, terrible characters shall examine. Ago lexical conditions get into a weeks. Barely trying results perform still hot men. Great kinds end also committees. Police should live only on the 	Doors canno	5216	0	1	1
+Additional, terrible characters shall examine. Ago lexical conditions get into a weeks. Barely trying results perform still hot men. Great kinds end also committees. Police should live only on the 	Important issues liv	5214	0	1	1
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+Adequately unemployed aspects ought to keep on a years. Years get somewhere sometimes late examples; laws must shape determined stones. Recently real decisions may cost now other female thousands. 	Important issues liv	5214	0	1	1
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+Adults throw close recent women. Orange, guilty libraries let earnings. Initiatives ought to walk. Simple, successful states might work eventually full orders. Formerly very 	Bad cards must make.	5206	0	1	1
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+Advantages go small. Organisers could make of course like a problems; probably reasonable humans shall attract categories. Agencies will enable much heavy matters. Stair		5206	0	1	1
+Advantages go small. Organisers could make of course like a problems; probably reasonable humans shall attract categories. Agencies will enable much heavy matters. Stair	Doors canno	5186	0	1	1
+Advantages go small. Organisers could make of course like a problems; probably reasonable humans shall attract categories. Agencies will enable much heavy matters. Stair	Important issues liv	5186	0	1	1
+Afraid years suspend much building		5171	0	1	1
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+Again judicial colours may blame fully british strange groups. Rules shall cover probably participants. W		5214	0	1	1
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+All 	Bad cards must make.	5175	0	1	1
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+Almost armed animals will maintain always pure, professional days; differe	Doors canno	5175	0	1	1
+Almost armed animals will maintain always pure, professional days; differe	Important issues liv	5175	0	1	1
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q73.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q73.out
new file mode 100644
index 0000000000..802aacaefe
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q73.out
@@ -0,0 +1,8 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q73 --
+Greene	Clarence		N	1541	5
+Peterson	Chantell	Miss	N	47551	5
+Scott	Anne	Dr.	N	86396	5
+Wheeler	Ashley	Mrs.	N	174863	5
+Dion	Alva	Mrs.	N	113766	4
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q74.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q74.out
new file mode 100644
index 0000000000..8fb21ea9ae
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q74.out
@@ -0,0 +1,95 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q74 --
+AAAAAAAAAMGDAAAA	Kenneth	Harlan
+AAAAAAAAANFAAAAA	Philip	Banks
+AAAAAAAAAOPFBAAA	Jerry	Fields
+AAAAAAAABLEIBAAA	Paula	Wakefield
+AAAAAAAABNBBAAAA	Irma	Smith
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+AAAAAAAAKKGEAAAA	Katie	Dunbar
+AAAAAAAAKLHHBAAA	Manuel	Castaneda
+AAAAAAAAKNAKAAAA	Gladys	Banks
+AAAAAAAAKOJJAAAA	Gracie	Mendoza
+AAAAAAAALFKKAAAA	Ignacio	Miller
+AAAAAAAALHMCAAAA	Brooke	Nelson
+AAAAAAAALIOPAAAA	Derek	Allen
+AAAAAAAALJNCBAAA	George	Gamez
+AAAAAAAAMDCAAAAA	Louann	Hamel
+AAAAAAAAMFFLAAAA	Margret	Gray
+AAAAAAAAMMOBBAAA	Margaret	Smith
+AAAAAAAANFBDBAAA	Vernice	Fernandez
+AAAAAAAANGDBBAAA	Carlos	Jewell
+AAAAAAAANIPLAAAA	Eric	Lawrence
+AAAAAAAANJAGAAAA	Allen	Hood
+AAAAAAAANJHCBAAA	Christopher	Schreiber
+AAAAAAAAOBADBAAA	Elizabeth	Burnham
+AAAAAAAAOCAJAAAA	Jenna	Staton
+AAAAAAAAOCDJAAAA	Nina	Sanchez
+AAAAAAAAOCICAAAA	Zachary	Pennington
+AAAAAAAAOCLBBAAA		
+AAAAAAAAOFLCAAAA	James	Taylor
+AAAAAAAAOPDLAAAA	Ann	Pence
+AAAAAAAAPDFBAAAA	Terrance	Banks
+AAAAAAAAPEHEBAAA	Edith	Molina
+AAAAAAAAPFCLAAAA	Felicia	Neville
+AAAAAAAAPJENAAAA	Ashley	Norton
+AAAAAAAAPKBCBAAA	Andrea	White
+AAAAAAAAPKIKAAAA	Wendy	Horvath
+AAAAAAAAPMMBBAAA	Paul	Jordan
+AAAAAAAAPPIBBAAA	Candice	Lee
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q75.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q75.out
new file mode 100644
index 0000000000..9d6b65f198
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q75.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q75 --
+2001	2002	9011004	11	9	730	6136	3927	-2209	-64592.86000000007
+2001	2002	9004008	4	9	454	6130	4012	-2118	-137401.99
+2001	2002	9006010	6	9	373	6584	4510	-2074	-56824.27000000002
+2001	2002	9003002	3	9	331	6314	4249	-2065	-162310.86999999994
+2001	2002	9016002	16	9	761	6098	4109	-1989	-75674.23000000004
+2001	2002	2001001	7	9	85	5689	3750	-1939	-79485.06999999992
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+2001	2002	9003002	3	9	1000	6152	4220	-1932	-51341.51999999999
+2001	2002	9009010	1	9	311	6980	5065	-1915	-166624.09999999986
+2001	2002	9003004	3	9	25	6382	4612	-1770	-109080.79999999999
+2001	2002	9008008	8	9	322	5391	3664	-1727	-58283.909999999974
+2001	2002	3004001	1	9	89	6307	4616	-1691	-105539.09999999989
+2001	2002	9014004	12	9	423	6427	4742	-1685	-66528.45000000013
+2001	2002	1001001	5	9	172	5642	3998	-1644	-65808.51999999999
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+2001	2002	8015003	4	9	289	6413	4905	-1508	-140884.2300000001
+2001	2002	9002008	2	9	202	5758	4287	-1471	-27415.179999999964
+2001	2002	9004002	4	9	55	5596	4196	-1400	-277.9199999998964
+2001	2002	9010002	10	9	432	5153	3767	-1386	-56643.30999999991
+2001	2002	9015003	15	9	278	5120	3745	-1375	-61503.399999999994
+2001	2002	5001001	10	9	136	5271	3918	-1353	-52691.740000000165
+2001	2002	9007002	7	9	207	6416	5088	-1328	-28312.290000000008
+2001	2002	9016004	8	9	114	5560	4263	-1297	-65169.90000000008
+2001	2002	9007004	7	9	968	5625	4330	-1295	-86120.29999999996
+2001	2002	9008008	8	9	535	6155	4861	-1294	-66647.47000000012
+2001	2002	3003001	1	9	175	6170	4892	-1278	-72260.64000000019
+2001	2002	9010004	3	9	948	5640	4387	-1253	-34985.50999999989
+2001	2002	9012008	12	9	249	5254	4002	-1252	-15484.879999999976
+2001	2002	9015008	15	9	281	5558	4315	-1243	10022.910000000178
+2001	2002	9006004	6	9	620	4822	3594	-1228	-19309.880000000005
+2001	2002	9010008	10	9	171	6500	5279	-1221	-73654.23000000001
+2001	2002	9004008	4	9	256	5366	4164	-1202	-93628.4599999999
+2001	2002	9008008	8	9	82	6042	4852	-1190	-78558.04999999976
+2001	2002	9014010	2	9	727	6579	5396	-1183	-45368.939999999886
+2001	2002	10002014	1	9	12	5662	4515	-1147	-47170.70000000001
+2001	2002	9005002	5	9	105	5650	4508	-1142	-75927.61000000002
+2001	2002	1001001	1	9	183	5538	4417	-1121	-34086.49000000008
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+2001	2002	9016010	16	9	662	5306	4197	-1109	-29664.5499999999
+2001	2002	2001001	1	9	151	6309	5200	-1109	-13881.979999999981
+2001	2002	7007001	12	9	221	6250	5147	-1103	8445.219999999943
+2001	2002	9010008	10	9	106	5739	4637	-1102	-39967.25999999995
+2001	2002	5001001	6	9	201	5714	4615	-1099	-2073.6100000001024
+2001	2002	9002004	2	9	578	5976	4881	-1095	-26222.70000000007
+2001	2002	9013010	12	9	28	5672	4579	-1093	-61365.09999999986
+2001	2002	9003002	3	9	238	5784	4694	-1090	-52094.93000000002
+2001	2002	9012010	12	9	4	6214	5128	-1086	-41244.90000000008
+2001	2002	4003001	6	9	356	5890	4815	-1075	-19594.679999999993
+2001	2002	9009002	9	9	633	5839	4786	-1053	-40163.89999999988
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+2001	2002	9003002	3	9	130	5708	4658	-1050	-121435.05000000013
+2001	2002	9011004	3	9	115	5101	4064	-1037	-38404.070000000065
+2001	2002	9006008	6	9	156	5824	4793	-1031	-16295.150000000081
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+2001	2002	9009008	9	9	3	5731	4718	-1013	-62660.48000000001
+2001	2002	10005001	5	9	65	5890	4879	-1011	-57577.00999999995
+2001	2002	9007008	7	9	122	6085	5077	-1008	-108804.86000000002
+2001	2002	9001008	1	9	258	5714	4706	-1008	-73311.06999999998
+2001	2002	9003008	3	9	382	5145	4143	-1002	-23005.48000000001
+2001	2002	2001001	1	9	177	5711	4718	-993	-45422.39999999985
+2001	2002	8002001	13	9	268	5521	4545	-976	-50681.48999999993
+2001	2002	9010008	10	9	9	5549	4578	-971	45104.78999999998
+2001	2002	5002001	2	9	230	5856	4887	-969	-25537.87000000011
+2001	2002	10015001	1	9	399	6188	5225	-963	-76365.3900000001
+2001	2002	9010004	10	9	110	5035	4082	-953	-81282.11999999979
+2001	2002	9013010	3	9	235	5771	4821	-950	-81516.67000000004
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+2001	2002	3003001	9	9	210	5703	4761	-942	-22794.72000000003
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+2001	2002	4001001	15	9	42	5385	4444	-941	-77081.27000000002
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+2001	2002	4002001	2	9	891	6168	5233	-935	-54473.39999999982
+2001	2002	10011013	11	9	123	6141	5207	-934	-77290.63000000012
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+2001	2002	9013010	13	9	288	5473	4631	-842	22917.879999999946
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+2001	2002	9015008	15	9	384	5348	4519	-829	-29937.399999999907
+2001	2002	1003001	3	9	242	5504	4697	-807	-10853.560000000143
+2001	2002	9011002	11	9	372	5085	4279	-806	-78551.75000000006
+2001	2002	9015002	15	9	224	5915	5112	-803	-46136.85000000009
+2001	2002	9015002	15	9	531	5831	5029	-802	-10846.840000000142
+2001	2002	2004001	16	9	443	5269	4475	-794	-28869.679999999877
+2001	2002	9003010	3	9	269	6297	5505	-792	-53827.19000000009
+2001	2002	9007002	7	9	306	5398	4608	-790	-67953.64000000004
+2001	2002	9012004	12	9	808	5796	5006	-790	-43025.360000000015
+2001	2002	6015001	15	9	543	5041	4252	-789	3631.4000000000815
+2001	2002	9004012	4	9	621	5974	5186	-788	52156.56000000003
+2001	2002	9010008	10	9	284	5382	4597	-785	22162.920000000042
+2001	2002	9011002	11	9	42	5750	4968	-782	-87125.33000000005
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q76.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q76.out
new file mode 100644
index 0000000000..ea2575c7e4
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q76.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q76 --
+catalog	cs_ship_addr_sk	1998	1	Books	14	24660.12
+catalog	cs_ship_addr_sk	1998	1	Children	5	5064.75
+catalog	cs_ship_addr_sk	1998	1	Electronics	13	31709.80
+catalog	cs_ship_addr_sk	1998	1	Home	12	11651.18
+catalog	cs_ship_addr_sk	1998	1	Jewelry	13	13102.23
+catalog	cs_ship_addr_sk	1998	1	Men	11	17458.37
+catalog	cs_ship_addr_sk	1998	1	Music	13	6741.65
+catalog	cs_ship_addr_sk	1998	1	Shoes	9	24531.24
+catalog	cs_ship_addr_sk	1998	1	Sports	9	19244.50
+catalog	cs_ship_addr_sk	1998	1	Women	13	24864.50
+catalog	cs_ship_addr_sk	1998	2	\N	1	1535.61
+catalog	cs_ship_addr_sk	1998	2	Books	14	16581.16
+catalog	cs_ship_addr_sk	1998	2	Children	12	26195.71
+catalog	cs_ship_addr_sk	1998	2	Electronics	9	7133.74
+catalog	cs_ship_addr_sk	1998	2	Home	15	44228.54
+catalog	cs_ship_addr_sk	1998	2	Jewelry	11	6804.54
+catalog	cs_ship_addr_sk	1998	2	Men	11	11700.11
+catalog	cs_ship_addr_sk	1998	2	Music	10	32062.82
+catalog	cs_ship_addr_sk	1998	2	Shoes	7	4682.58
+catalog	cs_ship_addr_sk	1998	2	Sports	7	5470.29
+catalog	cs_ship_addr_sk	1998	2	Women	5	14928.09
+catalog	cs_ship_addr_sk	1998	3	Books	20	22224.09
+catalog	cs_ship_addr_sk	1998	3	Children	19	18667.83
+catalog	cs_ship_addr_sk	1998	3	Electronics	15	21854.97
+catalog	cs_ship_addr_sk	1998	3	Home	22	24690.28
+catalog	cs_ship_addr_sk	1998	3	Jewelry	14	4586.41
+catalog	cs_ship_addr_sk	1998	3	Men	20	30700.69
+catalog	cs_ship_addr_sk	1998	3	Music	23	44816.68
+catalog	cs_ship_addr_sk	1998	3	Shoes	20	33458.73
+catalog	cs_ship_addr_sk	1998	3	Sports	25	35679.92
+catalog	cs_ship_addr_sk	1998	3	Women	22	48029.28
+catalog	cs_ship_addr_sk	1998	4	\N	2	839.65
+catalog	cs_ship_addr_sk	1998	4	Books	28	46283.99
+catalog	cs_ship_addr_sk	1998	4	Children	40	57305.90
+catalog	cs_ship_addr_sk	1998	4	Electronics	29	42656.07
+catalog	cs_ship_addr_sk	1998	4	Home	29	29708.36
+catalog	cs_ship_addr_sk	1998	4	Jewelry	29	24689.26
+catalog	cs_ship_addr_sk	1998	4	Men	36	64378.63
+catalog	cs_ship_addr_sk	1998	4	Music	35	51308.03
+catalog	cs_ship_addr_sk	1998	4	Shoes	27	10881.49
+catalog	cs_ship_addr_sk	1998	4	Sports	33	33380.56
+catalog	cs_ship_addr_sk	1998	4	Women	35	14347.96
+catalog	cs_ship_addr_sk	1999	1	Books	14	35278.57
+catalog	cs_ship_addr_sk	1999	1	Children	13	17183.17
+catalog	cs_ship_addr_sk	1999	1	Electronics	4	153.18
+catalog	cs_ship_addr_sk	1999	1	Home	11	19895.88
+catalog	cs_ship_addr_sk	1999	1	Jewelry	9	1341.90
+catalog	cs_ship_addr_sk	1999	1	Men	9	6625.86
+catalog	cs_ship_addr_sk	1999	1	Music	10	27861.23
+catalog	cs_ship_addr_sk	1999	1	Shoes	15	5365.20
+catalog	cs_ship_addr_sk	1999	1	Sports	9	10745.83
+catalog	cs_ship_addr_sk	1999	1	Women	11	15388.74
+catalog	cs_ship_addr_sk	1999	2	Books	6	3659.91
+catalog	cs_ship_addr_sk	1999	2	Children	13	6066.50
+catalog	cs_ship_addr_sk	1999	2	Electronics	10	14915.50
+catalog	cs_ship_addr_sk	1999	2	Home	13	12035.96
+catalog	cs_ship_addr_sk	1999	2	Jewelry	6	23975.00
+catalog	cs_ship_addr_sk	1999	2	Men	13	16414.16
+catalog	cs_ship_addr_sk	1999	2	Music	13	6061.53
+catalog	cs_ship_addr_sk	1999	2	Shoes	10	12500.71
+catalog	cs_ship_addr_sk	1999	2	Sports	11	2181.52
+catalog	cs_ship_addr_sk	1999	2	Women	11	16390.31
+catalog	cs_ship_addr_sk	1999	3	Books	18	29809.59
+catalog	cs_ship_addr_sk	1999	3	Children	16	12816.74
+catalog	cs_ship_addr_sk	1999	3	Electronics	17	36415.09
+catalog	cs_ship_addr_sk	1999	3	Home	15	19664.04
+catalog	cs_ship_addr_sk	1999	3	Jewelry	20	23257.68
+catalog	cs_ship_addr_sk	1999	3	Men	19	20150.72
+catalog	cs_ship_addr_sk	1999	3	Music	20	12062.24
+catalog	cs_ship_addr_sk	1999	3	Shoes	13	14924.61
+catalog	cs_ship_addr_sk	1999	3	Sports	24	56456.44
+catalog	cs_ship_addr_sk	1999	3	Women	17	6958.54
+catalog	cs_ship_addr_sk	1999	4	\N	1	\N
+catalog	cs_ship_addr_sk	1999	4	Books	27	50890.95
+catalog	cs_ship_addr_sk	1999	4	Children	36	30608.13
+catalog	cs_ship_addr_sk	1999	4	Electronics	28	59307.37
+catalog	cs_ship_addr_sk	1999	4	Home	46	51713.78
+catalog	cs_ship_addr_sk	1999	4	Jewelry	34	44238.07
+catalog	cs_ship_addr_sk	1999	4	Men	19	17925.34
+catalog	cs_ship_addr_sk	1999	4	Music	28	14816.79
+catalog	cs_ship_addr_sk	1999	4	Shoes	35	48226.50
+catalog	cs_ship_addr_sk	1999	4	Sports	32	35012.19
+catalog	cs_ship_addr_sk	1999	4	Women	30	24033.32
+catalog	cs_ship_addr_sk	2000	1	Books	15	50786.51
+catalog	cs_ship_addr_sk	2000	1	Children	6	5623.72
+catalog	cs_ship_addr_sk	2000	1	Electronics	13	35869.18
+catalog	cs_ship_addr_sk	2000	1	Home	14	11212.87
+catalog	cs_ship_addr_sk	2000	1	Jewelry	9	16751.45
+catalog	cs_ship_addr_sk	2000	1	Men	14	34465.53
+catalog	cs_ship_addr_sk	2000	1	Music	9	23103.03
+catalog	cs_ship_addr_sk	2000	1	Shoes	9	2854.29
+catalog	cs_ship_addr_sk	2000	1	Sports	5	159.04
+catalog	cs_ship_addr_sk	2000	1	Women	8	8663.29
+catalog	cs_ship_addr_sk	2000	2	Books	14	18943.05
+catalog	cs_ship_addr_sk	2000	2	Children	8	7309.74
+catalog	cs_ship_addr_sk	2000	2	Electronics	9	11856.27
+catalog	cs_ship_addr_sk	2000	2	Home	8	10107.78
+catalog	cs_ship_addr_sk	2000	2	Jewelry	9	19113.02
+catalog	cs_ship_addr_sk	2000	2	Men	12	41513.90
+catalog	cs_ship_addr_sk	2000	2	Music	10	7181.12
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q77.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q77.out
new file mode 100644
index 0000000000..734ff26cd0
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q77.out
@@ -0,0 +1,47 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q77 --
+\N	\N	562937653.47	12490154.95	-100351224.00
+catalog channel	\N	538912.55	2050279.74	-1383554.73
+catalog channel	\N	404410817.75	8201118.96	-42762489.82
+catalog channel	1	132885061.65	2050279.74	-12674076.58
+catalog channel	2	140503047.65	2050279.74	-14906564.08
+catalog channel	5	130483795.90	2050279.74	-13798294.43
+store channel	\N	117249373.32	3173554.99	-52383291.20
+store channel	1	20390161.35	562762.31	-9133254.67
+store channel	2	19807085.95	539649.43	-8817821.00
+store channel	4	19599593.20	557973.00	-8389920.41
+store channel	7	19480205.51	520479.41	-8861241.78
+store channel	8	18636331.60	472731.69	-8409599.72
+store channel	10	19335995.71	519959.15	-8771453.62
+web channel	\N	41277462.40	1115481.00	-5205442.98
+web channel	1	1226811.57	28406.98	-227375.53
+web channel	2	1191229.91	99179.48	-264992.86
+web channel	5	1467083.19	21625.36	-147366.78
+web channel	7	1343208.21	67708.76	-200969.21
+web channel	8	1262065.97	46749.46	-271001.70
+web channel	11	1425934.76	10034.84	-84693.54
+web channel	13	1335813.60	62142.91	-218022.02
+web channel	14	1469352.58	50742.65	-197789.09
+web channel	17	1219451.02	28732.85	-205497.30
+web channel	19	1343058.55	24108.59	-175397.06
+web channel	20	1511303.97	42538.28	-89439.28
+web channel	23	1409483.07	37116.42	-89855.78
+web channel	25	1370755.17	48916.38	-207512.02
+web channel	26	1465712.89	48072.56	-157007.72
+web channel	29	1407813.82	19233.11	-188381.47
+web channel	31	1369226.19	25494.42	-180972.91
+web channel	32	1166947.23	50731.53	-189061.60
+web channel	35	1400811.57	22363.43	-189390.67
+web channel	37	1407716.73	32534.27	-127244.28
+web channel	38	1444241.42	41815.25	-135372.36
+web channel	41	1492530.29	19599.96	-101110.49
+web channel	43	1343104.79	41175.01	-227340.10
+web channel	44	1416507.16	37134.99	-274620.10
+web channel	47	1449718.94	15989.92	-105473.72
+web channel	49	1414898.83	45004.31	-146020.31
+web channel	50	1319375.84	28284.26	-151036.44
+web channel	53	1389137.89	24570.34	-120694.61
+web channel	55	1463362.30	38157.61	-154431.83
+web channel	56	1355553.42	46633.14	-164174.45
+web channel	59	1395251.52	10683.93	-213197.75
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q78.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q78.out
new file mode 100644
index 0000000000..5d67200bef
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q78.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q78 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q79.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q79.out
new file mode 100644
index 0000000000..af15711a5b
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q79.out
@@ -0,0 +1,103 @@
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+-- !pipeline_q79 --
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q80.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q80.out
new file mode 100644
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+catalog channel	catalog_pageAAAAAAAADGABAAAA	9050.23	3062.68	-2935.52
+catalog channel	catalog_pageAAAAAAAADGCBAAAA	7789.42	0.00	-3868.94
+catalog channel	catalog_pageAAAAAAAADHABAAAA	25558.98	0.00	-6817.76
+catalog channel	catalog_pageAAAAAAAADHCBAAAA	5245.91	65.34	-1734.29
+catalog channel	catalog_pageAAAAAAAADICBAAAA	0.00	0.00	-2246.10
+catalog channel	catalog_pageAAAAAAAADJCBAAAA	7721.15	0.00	3313.85
+catalog channel	catalog_pageAAAAAAAADKCBAAAA	5463.32	1677.60	874.45
+catalog channel	catalog_pageAAAAAAAADKPAAAAA	30068.71	0.00	-8011.27
+catalog channel	catalog_pageAAAAAAAADLCBAAAA	1146.19	0.00	-1287.95
+catalog channel	catalog_pageAAAAAAAADLPAAAAA	35314.26	0.00	-5055.96
+catalog channel	catalog_pageAAAAAAAADMCBAAAA	2420.23	0.00	-1610.01
+catalog channel	catalog_pageAAAAAAAADMPAAAAA	32509.00	737.40	-14350.75
+catalog channel	catalog_pageAAAAAAAADNCBAAAA	9986.56	5534.72	1208.35
+catalog channel	catalog_pageAAAAAAAADNPAAAAA	1953.82	0.00	220.42
+catalog channel	catalog_pageAAAAAAAADOCBAAAA	3356.59	62.99	-4084.30
+catalog channel	catalog_pageAAAAAAAADOPAAAAA	5090.89	0.00	-2319.86
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q81.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q81.out
new file mode 100644
index 0000000000..e0217c96ac
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q81.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q81 --
+AAAAAAAAABBEAAAA	Dr.	Oscar	Fox	183	15th 	Ln	Suite P	Greenwood	Clay County	GA	38828	United States	-5.00	condo	4607.92
+AAAAAAAAADCEBAAA	Sir	Billy	Bell	690	4th Fifth	Ct.	Suite 390	Hillcrest	Spalding County	GA	33003	United States	-5.00	condo	5567.44
+AAAAAAAAAFBGAAAA	Mrs.	Erika	Buford	527	1st 	Ln	Suite 380	Lakewood	Jasper County	GA	38877	United States	-5.00	single family	5412.16
+AAAAAAAAAFLABAAA	Mr.	Richard	James	40	2nd Miller	Road	Suite 190	Arlington	Morgan County	GA	36557	United States	-5.00	apartment	2249.10
+AAAAAAAAAHOEAAAA	Mr.	Michael	Yates	349	Elm South	ST	Suite 30	Blanchard	Talbot County	GA	35985	United States	-5.00	single family	1810.13
+AAAAAAAAAIECBAAA	Miss	Elizabeth	Schneider	131	4th 	Street	Suite N	Forest Hills	Ware County	GA	39237	United States	-5.00	condo	9973.72
+AAAAAAAAAIEHBAAA	Miss	Tamara	Chestnut	934	13th Cherry	Blvd	Suite O	Antioch	Haralson County	GA	38605	United States	-5.00	condo	1639.26
+AAAAAAAAAJLNAAAA	Dr.	Sheree	Bragg	664	Elm 4th	Ave	Suite 320	Glenwood	McDuffie County	GA	33511	United States	-5.00	condo	3012.45
+AAAAAAAAAJNEBAAA	Dr.	David	Hamilton	108	Spring Wilson	Ct.	Suite F	Glenwood	Macon County	GA	33511	United States	-5.00	apartment	2567.79
+AAAAAAAAAKJOAAAA	Dr.	Gus	Stewart	883	Valley 	Drive	Suite J	Macedonia	Lanier County	GA	31087	United States	-5.00	single family	3530.28
+AAAAAAAAAMFBBAAA	Mr.	Jackson		177	8th Walnut	Ct.	Suite 300	Riverview	Jenkins County	GA	39003	United States	-5.00	single family	4875.86
+AAAAAAAAAMPKAAAA	Ms.	Elizabeth	Booth	344	Jackson North	Drive	Suite 100	Woodville	Fayette County	GA	34289	United States	-5.00	single family	2372.60
+AAAAAAAAANCLAAAA	Mr.	Raymond	Dixon	189	Jackson 7th	Parkway	Suite 360	Lakewood	Dade County	GA	38877	United States	-5.00	single family	4655.74
+AAAAAAAAANJIAAAA	Dr.	Dennis	Roper	600	Walnut 	Pkwy	Suite 140	Plainview	Barrow County	GA	33683	United States	-5.00	apartment	3500.40
+AAAAAAAAANLABAAA	Sir	Thomas	Zimmerman	496	Forest 	ST	Suite L	Saratoga	Lumpkin County	GA	32123	United States	-5.00	apartment	9193.95
+AAAAAAAAAODFAAAA	Mrs.	Kathy	Villegas	156	4th Sunset	Cir.	Suite I	Red Hill	Appling County	GA	34338	United States	-5.00	single family	2106.00
+AAAAAAAAAOEBBAAA	Sir	Benjamin	Parker	698	6th 	Drive	Suite O	Plainview	Screven County	GA	33683	United States	-5.00	apartment	3891.38
+AAAAAAAAAOEBBAAA	Sir	Benjamin	Parker	698	6th 	Drive	Suite O	Plainview	Screven County	GA	33683	United States	-5.00	apartment	9787.55
+AAAAAAAAAPBBBAAA	Sir	Donald	Littlejohn	467	Lake 	Ln	Suite 20	Riverside	Turner County	GA	39231	United States	-5.00	single family	4831.52
+AAAAAAAABABBBAAA	Mr.	David	Roe	521	Jefferson 	Parkway	Suite 120	Enterprise	Wilcox County	GA	31757	United States	-5.00	apartment	4079.80
+AAAAAAAABACGAAAA		Catherine		463	Miller 	Lane	Suite Y	Woodland	Walton County	GA	34854	United States	-5.00	apartment	8619.84
+AAAAAAAABAJHAAAA	Mrs.	Doris	Gillespie	179	Washington Chestnut	Dr.	Suite W	Jackson	Columbia County	GA	39583	United States	-5.00	condo	4617.58
+AAAAAAAABANGBAAA	Mr.	Ralph	Condon	755	Davis 	Blvd	Suite 280	Antioch	Pike County	GA	38605	United States	-5.00	condo	14966.24
+AAAAAAAABBHKAAAA	Mrs.	Kristin	Grant	870	Ash Park	Ave	Suite 300	Lakeview	Harris County	GA	38579	United States	-5.00	single family	3580.44
+AAAAAAAABBOBBAAA	Mr.	Herman	Rivera	396	Walnut 	Cir.	Suite P	Athens	Baldwin County	GA	36796	United States	-5.00	single family	4608.96
+AAAAAAAABCAOAAAA	Dr.	Brenda	Bell	562	7th Park	Road	Suite 320	Edgewood	Troup County	GA	30069	United States	-5.00	apartment	3721.03
+AAAAAAAABCIMAAAA	Dr.	Becky	Seals	174	Park Park	Ct.	Suite H	Green Acres	Schley County	GA	37683	United States	-5.00	single family	2980.15
+AAAAAAAABDHFAAAA	Mr.	Charles	Miller	531	Church 14th	Circle	Suite 60	Ashland	Franklin County	GA	34244	United States	-5.00	apartment	3241.85
+AAAAAAAABEHABAAA	Dr.	Gregory		391	Seventh 	Road	Suite Q	Oakwood	Liberty County	GA	30169	United States	-5.00	single family	3520.33
+AAAAAAAABFNHBAAA	Ms.	Janet	York	972	2nd Third	Road	Suite 120	Crossroads	Jackson County	GA	30534	United States	-5.00	apartment	5047.25
+AAAAAAAABGDGBAAA	Sir	Brian	Adams	915	Jackson 	RD	Suite O	Concord	Columbia County	GA	34107	United States	-5.00	apartment	2912.27
+AAAAAAAABGLCBAAA	Dr.	Joseph	Torres	103	Highland 7th	Court	Suite I	Wilson	Jenkins County	GA	36971	United States	-5.00	single family	1842.89
+AAAAAAAABHOFAAAA	Ms.	Anthony		518	Meadow Smith	Ct.	Suite C	Riverdale	Bleckley County	GA	39391	United States	-5.00	single family	1878.44
+AAAAAAAABIMMAAAA	Dr.	Shanna	Mcrae	764	Walnut 	Street	Suite B	Brownsville	Lumpkin County	GA	39310	United States	-5.00	condo	7237.05
+AAAAAAAABINIAAAA	Dr.	Jose	Reynolds	571	Main 	Ave	Suite B	Mount Zion	Troup County	GA	38054	United States	-5.00	apartment	2463.65
+AAAAAAAABJMEAAAA	Miss	Deborah	Sheehan	450	Willow 	Cir.	Suite G	Marion	Franklin County	GA	30399	United States	-5.00	condo	6623.88
+AAAAAAAABKAIAAAA	Miss	Ruth	Rodriguez	706	Williams 	Pkwy	Suite Y	Antioch	Lanier County	GA	38605	United States	-5.00	single family	3025.05
+AAAAAAAABMBLAAAA	Ms.	Ava	Jackson	601	Fourth Washington	Way	Suite 440	Glendale	Cherokee County	GA	33951	United States	-5.00	apartment	1637.46
+AAAAAAAABMBLAAAA	Ms.	Ava	Jackson	601	Fourth Washington	Way	Suite 440	Glendale	Cherokee County	GA	33951	United States	-5.00	apartment	2373.14
+AAAAAAAABMFDAAAA	Mrs.	Megan	Webster	124	11th 	Pkwy	Suite K	Shady Grove	Miller County	GA	32812	United States	-5.00	single family	3059.27
+AAAAAAAABNMGBAAA	Mrs.	Wanda	Shumate	422	Pine 3rd	Cir.	Suite 170	Crossroads	Berrien County	GA	30534	United States	-5.00	single family	1699.41
+AAAAAAAABNMIAAAA	Miss	Bobbi	Erickson	731	Jefferson 	Parkway	Suite H	Mountain View	Taylor County	GA	34466	United States	-5.00	apartment	2925.34
+AAAAAAAABOAPAAAA	Ms.	Karen	Burt	794	Sunset Sunset	Drive	Suite 470	Glendale	Calhoun County	GA	33951	United States	-5.00	condo	8807.07
+AAAAAAAABOCDBAAA	Ms.	Flora	Messer	608	10th 	Court	Suite A	Highland Park	Dooly County	GA	36534	United States	-5.00	single family	3255.56
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+AAAAAAAACCBOAAAA	Sir	Patrick	Miller	577	Church 	Ave	Suite 110	Bethel	White County	GA	35281	United States	-5.00	single family	2927.35
+AAAAAAAACDOPAAAA	Mrs.	Carolyn	Singer	399	Second 	Boulevard	Suite O	White Oak	Wayne County	GA	36668	United States	-5.00	condo	4011.31
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+AAAAAAAACJCHAAAA	Mr.	Wilbur	Mullins	119	Eigth 	Dr.	Suite W	Fairview	Wilkes County	GA	35709	United States	-5.00	single family	4861.83
+AAAAAAAACJJKAAAA	Mr.	Ramon	Zaragoza	934	Park 	Lane	Suite K	Lakewood	Muscogee County	GA	38877	United States	-5.00	condo	9022.65
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+AAAAAAAACODJAAAA	Dr.	Neal	Nash	95		Street	Suite 110	Spring Hill	Murray County	GA	36787		-5.00		2144.31
+AAAAAAAACOFIBAAA	Mr.	Fred	Morales	687	5th Park	Cir.	Suite X	Fairview	Murray County	GA	35709	United States	-5.00	condo	1936.70
+AAAAAAAACOHIAAAA	Mr.	Roger	Grider	191	Williams Second	Avenue	Suite 420	Brownsville	Coweta County	GA	39310	United States	-5.00	single family	5782.43
+AAAAAAAACPBABAAA	Miss	Lucile	Anderson	149	View Williams	Ct.	Suite L	Mount Pleasant	Washington County	GA	31933	United States	-5.00	single family	2600.59
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+AAAAAAAADBEIBAAA	Miss	Erica	Hill	396	Maple Johnson	Avenue	Suite R	Crossroads	Hall County	GA	30534	United States	-5.00	condo	4500.01
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+AAAAAAAADDENAAAA	Sir	Alejandro	Doyle	916	Cherry Elm	ST	Suite 110	Providence	Clay County	GA	36614	United States	-5.00	single family	3357.92
+AAAAAAAADEGFAAAA	Sir	Jesse	Johnson	725	Laurel First	Parkway	Suite O	Pleasant Valley	Candler County	GA	32477	United States	-5.00	condo	4969.77
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+AAAAAAAADJABAAAA	Mrs.	Charity	Arroyo	158	Highland Elm	Ln	Suite K	Woodland	Pickens County	GA	34854	United States	-5.00	apartment	2443.75
+AAAAAAAADJJMAAAA	Dr.	Joseph	Burns	339	12th 	Blvd	Suite S	Hamilton	Banks County	GA	32808	United States	-5.00	condo	2670.84
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+AAAAAAAADKKGAAAA	Sir	Robert	Patrick	638	Fourth 	Cir.	Suite 250	Lebanon	Clay County	GA	32898	United States	-5.00	condo	9049.72
+AAAAAAAADMDHAAAA	Mr.	Samuel	Wells	877	Highland East	Road	Suite I	Valley View	Bartow County	GA	35124	United States	-5.00	single family	4490.95
+AAAAAAAADNIBBAAA	Miss	Sharon	Prince	955	Walnut Ridge	Boulevard	Suite W	Cedar Grove	Troup County	GA	30411	United States	-5.00	condo	2158.11
+AAAAAAAADNOGBAAA	Dr.	David	Lofton	327	11th Central	Parkway	Suite D	White Oak	Glynn County	GA	36668	United States	-5.00	single family	1971.52
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+AAAAAAAADOFNAAAA	Mrs.	Sebrina	Jacobs	611	Center 	Avenue	Suite 210	Glenwood	Ware County	GA	33511	United States	-5.00	condo	5738.75
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+AAAAAAAAECOGBAAA	Dr.	David	Lamb	436	Second Elm	Way	Suite 30	Green Acres	Laurens County	GA	37683	United States	-5.00	single family	4778.71
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+AAAAAAAAEJFNAAAA	Dr.	Chad	Baldwin	404	Cedar 	Ct.	Suite Y	Enterprise	Treutlen County	GA	31757	United States	-5.00	apartment	1737.56
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+AAAAAAAAEKGABAAA	Sir	Steven	Thurman	638	Park Green	Ct.	Suite 270	Lakeside	Wayne County	GA	39532	United States	-5.00	single family	1662.14
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+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q82.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q82.out
new file mode 100644
index 0000000000..3d3bca59c5
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q82.out
@@ -0,0 +1,5 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q82 --
+AAAAAAAAECMCAAAA	Rather vast companies pose quiet, actual carers. Close times take only simple possibilities. Current events might say only on a foundation	67.28
+AAAAAAAALIHCAAAA	Things select increased views. Tools imagine for example; ever likely developments live so scottish benefits. Soft, short-term services should give indeed good illu	86.90
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q83.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q83.out
new file mode 100644
index 0000000000..c87c21b970
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q83.out
@@ -0,0 +1,24 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q83 --
+AAAAAAAAANECAAAA	34	18.579234972677593	9	4.918032786885246	18	9.836065573770492	20.333333333
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diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q84.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q84.out
new file mode 100644
index 0000000000..a907da1910
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q84.out
@@ -0,0 +1,28 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q84 --
+AAAAAAAAAIPGAAAA	Carter, Rodney
+AAAAAAAAAKMBBAAA	Mcarthur, Emma
+AAAAAAAACBNHBAAA	Wells, Ron
+AAAAAAAADBMEAAAA	Vera, Tina
+AAAAAAAADBMEAAAA	Vera, Tina
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+AAAAAAAAFKAHAAAA	Batiste, Ernest
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+AAAAAAAAIAODBAAA	Murray, Karen
+AAAAAAAAIEOKAAAA	Solomon, Clyde
+AAAAAAAAIIBOAAAA	Owens, David
+AAAAAAAAIPDCAAAA	Wallace, Eric
+AAAAAAAAIPIMAAAA	Hayward, Benjamin
+AAAAAAAAJCIKAAAA	Ramos, Donald
+AAAAAAAAKFJEAAAA	Roberts, Yvonne
+AAAAAAAAKPGBBAAA	Moore, 
+AAAAAAAALCLABAAA	Whitaker, Lettie
+AAAAAAAAMGMEAAAA	Sharp, Michael
+AAAAAAAAMIGBBAAA	Montgomery, Jesenia
+AAAAAAAAMPDKAAAA	Lopez, Isabel
+AAAAAAAANEOMAAAA	Powell, Linda
+AAAAAAAANKPCAAAA	Shaffer, Sergio
+AAAAAAAANOCKAAAA	Vargas, James
+AAAAAAAAOGJEBAAA	Owens, Denice
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q85.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q85.out
new file mode 100644
index 0000000000..92077d88a4
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q85.out
@@ -0,0 +1,9 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q85 --
+Gift exchange	76.0	464.36	8.62
+Not the product that	70.0	876.67	46.67
+Parts missing	7.0	129.42	38.65
+reason 23	47.0	734.61	6.17
+reason 25	5.0	48.94	53.14
+reason 28	8.0	306.20	37.06
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q86.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q86.out
new file mode 100644
index 0000000000..1c0881d892
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q86.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q86 --
+325552630.64	\N	\N	2	1
+33602545.41	Shoes	\N	1	1
+33399717.80	Music	\N	1	2
+33061835.62	Women	\N	1	3
+32942681.04	Books	\N	1	4
+32233369.67	Men	\N	1	5
+32135907.22	Electronics	\N	1	6
+32027085.12	Jewelry	\N	1	7
+31908139.65	Sports	\N	1	8
+31877378.15	Children	\N	1	9
+31428816.29	Home	\N	1	10
+935154.67	\N	\N	1	11
+476839.60	\N		0	1
+88724.30	\N	womens	0	2
+43184.49	\N	glassware	0	3
+40345.26	\N	flatware	0	4
+36841.11	\N	baseball	0	5
+34642.53	\N	swimwear	0	6
+33866.09	\N	scanners	0	7
+32159.86	\N	archery	0	8
+30361.13	\N	outdoor	0	9
+29969.36	\N	dresses	0	10
+25780.80	\N	pants	0	11
+25714.74	\N	sports-apparel	0	12
+21122.88	\N	tennis	0	13
+15602.52	\N	semi-precious	0	14
+2981755.34	Books	history	0	1
+2533681.90	Books	romance	0	2
+2513406.90	Books	computers	0	3
+2359621.74	Books	fiction	0	4
+2220829.36	Books	home repair	0	5
+2132619.93	Books	reference	0	6
+2023862.05	Books	travel	0	7
+1938302.12	Books	parenting	0	8
+1916049.65	Books	science	0	9
+1904474.64	Books	business	0	10
+1903280.30	Books	sports	0	11
+1819360.79	Books	self-help	0	12
+1817324.19	Books	mystery	0	13
+1698653.10	Books	entertainments	0	14
+1641874.01	Books	cooking	0	15
+1453516.79	Books	arts	0	16
+84068.23	Books		0	17
+8603692.12	Children	infants	0	1
+7982811.62	Children	toddlers	0	2
+7716135.48	Children	school-uniforms	0	3
+7529560.01	Children	newborn	0	4
+45178.92	Children		0	5
+2625503.90	Electronics	dvd/vcr players	0	1
+2351244.66	Electronics	televisions	0	2
+2283231.32	Electronics	memory	0	3
+2262599.89	Electronics	stereo	0	4
+2257811.83	Electronics	karoke	0	5
+2235218.27	Electronics	monitors	0	6
+2228844.29	Electronics	scanners	0	7
+1948029.88	Electronics	wireless	0	8
+1894729.95	Electronics	disk drives	0	9
+1838653.07	Electronics	automotive	0	10
+1793728.96	Electronics	portable	0	11
+1768939.49	Electronics	cameras	0	12
+1733994.95	Electronics	musical	0	13
+1730490.24	Electronics	personal	0	14
+1670511.23	Electronics	camcorders	0	15
+1512375.29	Electronics	audio	0	16
+2408305.34	Home	paint	0	1
+2349779.93	Home	curtains/drapes	0	2
+2306676.63	Home	bedding	0	3
+2284168.74	Home	flatware	0	4
+2269734.86	Home	glassware	0	5
+2112112.73	Home	lighting	0	6
+2085612.39	Home	bathroom	0	7
+1924834.23	Home	mattresses	0	8
+1868688.89	Home	tables	0	9
+1820604.22	Home	furniture	0	10
+1816997.04	Home	decor	0	11
+1805905.27	Home	kids	0	12
+1788142.30	Home	blinds/shades	0	13
+1570699.11	Home	accent	0	14
+1503088.13	Home	rugs	0	15
+1457642.85	Home	wallpaper	0	16
+55823.63	Home		0	17
+2754963.88	Jewelry	jewelry boxes	0	1
+2346470.04	Jewelry	pendants	0	2
+2308524.13	Jewelry	rings	0	3
+2216602.19	Jewelry	custom	0	4
+2141484.71	Jewelry	gold	0	5
+2105589.88	Jewelry	estate	0	6
+2105536.74	Jewelry	womens watch	0	7
+2057556.93	Jewelry	mens watch	0	8
+1964085.08	Jewelry	costume	0	9
+1838748.38	Jewelry	birdal	0	10
+1820456.21	Jewelry	earings	0	11
+1811297.48	Jewelry	loose stones	0	12
+1738088.37	Jewelry	diamonds	0	13
+1636759.38	Jewelry	semi-precious	0	14
+1625306.26	Jewelry	bracelets	0	15
+1457032.40	Jewelry	consignment	0	16
+98583.06	Jewelry		0	17
+8968117.21	Men	shirts	0	1
+8079270.31	Men	sports-apparel	0	2
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q87.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q87.out
new file mode 100644
index 0000000000..c2a87b6fff
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q87.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q87 --
+47298
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q88.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q88.out
new file mode 100644
index 0000000000..a4c58ea0f9
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q88.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q88 --
+2334	4726	4564	7538	7115	3960	4129	4533
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q89.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q89.out
new file mode 100644
index 0000000000..c15c3dd8f7
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q89.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q89 --
+Men	shirts	importoimporto #1	ese	Unknown	3	2070.65	5492.81
+Men	shirts	importoimporto #1	ought	Unknown	6	2152.15	5045.75
+Men	shirts	importoimporto #1	able	Unknown	2	2333.43	5194.81
+Men	shirts	importoimporto #1	ese	Unknown	6	2691.33	5492.81
+Men	shirts	importoimporto #1	ought	Unknown	2	2253.70	5045.75
+Women	dresses	amalgamalg #1	able	Unknown	6	1761.81	4507.24
+Men	shirts	importoimporto #1	ation	Unknown	3	2672.68	5410.91
+Men	shirts	importoimporto #1	ation	Unknown	5	2677.81	5410.91
+Men	shirts	importoimporto #1	eing	Unknown	5	2356.88	5074.07
+Men	shirts	importoimporto #1	ese	Unknown	4	2784.11	5492.81
+Women	dresses	amalgamalg #1	ought	Unknown	7	1926.67	4619.70
+Women	dresses	amalgamalg #1	ought	Unknown	1	2065.94	4619.70
+Men	shirts	importoimporto #1	ation	Unknown	6	2881.34	5410.91
+Men	shirts	importoimporto #1	able	Unknown	3	2674.91	5194.81
+Women	dresses	amalgamalg #1	ation	Unknown	2	2192.31	4690.89
+Men	shirts	importoimporto #1	bar	Unknown	5	2475.90	4973.50
+Women	dresses	amalgamalg #1	ese	Unknown	2	2146.52	4642.99
+Men	shirts	importoimporto #1	eing	Unknown	2	2588.02	5074.07
+Women	dresses	amalgamalg #1	bar	Unknown	7	2428.75	4914.11
+Women	dresses	amalgamalg #1	bar	Unknown	3	2451.51	4914.11
+Men	shirts	importoimporto #1	ation	Unknown	7	2951.67	5410.91
+Men	shirts	importoimporto #1	able	Unknown	1	2765.75	5194.81
+Women	dresses	amalgamalg #1	able	Unknown	2	2082.54	4507.24
+Men	shirts	importoimporto #1	ation	Unknown	4	2988.08	5410.91
+Men	shirts	importoimporto #1	ought	Unknown	7	2667.92	5045.75
+Women	dresses	amalgamalg #1	bar	Unknown	4	2549.27	4914.11
+Women	dresses	amalgamalg #1	ese	Unknown	7	2307.00	4642.99
+Women	dresses	amalgamalg #1	eing	Unknown	4	2204.15	4528.82
+Men	shirts	importoimporto #1	able	Unknown	7	2880.34	5194.81
+Men	shirts	importoimporto #1	ese	Unknown	5	3210.74	5492.81
+Women	dresses	amalgamalg #1	ation	Unknown	4	2418.39	4690.89
+Men	shirts	importoimporto #1	eing	Unknown	4	2833.40	5074.07
+Women	dresses	amalgamalg #1	bar	Unknown	2	2692.79	4914.11
+Men	shirts	importoimporto #1	eing	Unknown	6	2854.62	5074.07
+Women	dresses	amalgamalg #1	eing	Unknown	6	2314.71	4528.82
+Men	shirts	importoimporto #1	ought	Unknown	5	2834.94	5045.75
+Women	dresses	amalgamalg #1	ation	Unknown	5	2480.25	4690.89
+Women	dresses	amalgamalg #1	able	Unknown	7	2302.21	4507.24
+Women	dresses	amalgamalg #1	ation	Unknown	6	2501.15	4690.89
+Men	shirts	importoimporto #1	ese	Unknown	2	3307.78	5492.81
+Men	shirts	importoimporto #1	able	Unknown	5	3016.43	5194.81
+Men	shirts	importoimporto #1	able	Unknown	4	3040.23	5194.81
+Women	dresses	amalgamalg #1	eing	Unknown	5	2420.31	4528.82
+Women	dresses	amalgamalg #1	ese	Unknown	5	2535.80	4642.99
+Women	dresses	amalgamalg #1	ought	Unknown	2	2521.70	4619.70
+Men	shirts	importoimporto #1	bar	Unknown	3	2877.96	4973.50
+Women	dresses	amalgamalg #1	ese	Unknown	3	2577.15	4642.99
+Women	dresses	amalgamalg #1	ation	Unknown	7	2637.27	4690.89
+Men	shirts	importoimporto #1	ation	Unknown	1	3380.16	5410.91
+Women	dresses	amalgamalg #1	eing	Unknown	7	2524.32	4528.82
+Men	shirts	importoimporto #1	bar	Unknown	4	2986.70	4973.50
+Women	dresses	amalgamalg #1	ought	Unknown	4	2648.80	4619.70
+Men	shirts	importoimporto #1	bar	Unknown	7	3026.41	4973.50
+Women	dresses	amalgamalg #1	ation	Unknown	3	2751.11	4690.89
+Men	shirts	importoimporto #1	bar	Unknown	2	3073.45	4973.50
+Men	shirts	importoimporto #1	eing	Unknown	3	3179.96	5074.07
+Women	dresses	amalgamalg #1	ation	Unknown	1	2797.07	4690.89
+Women	dresses	amalgamalg #1	able	Unknown	1	2621.05	4507.24
+Women	dresses	amalgamalg #1	ese	Unknown	4	2808.36	4642.99
+Women	dresses	amalgamalg #2	bar	Unknown	6	680.47	2506.96
+Men	shirts	importoimporto #1	ation	Unknown	2	3591.65	5410.91
+Women	dresses	amalgamalg #1	able	Unknown	3	2706.15	4507.24
+Women	dresses	amalgamalg #2	eing	Unknown	2	773.08	2569.34
+Men	shirts	importoimporto #1	bar	Unknown	6	3199.08	4973.50
+Men	shirts	importoimporto #1	ese	Unknown	7	3754.33	5492.81
+Women	dresses	amalgamalg #1	bar	Unknown	5	3190.77	4914.11
+Women	dresses	amalgamalg #1	eing	Unknown	2	2841.88	4528.82
+Men	shirts	importoimporto #1	able	Unknown	6	3538.25	5194.81
+Women	dresses	amalgamalg #1	ese	Unknown	6	3006.94	4642.99
+Women	dresses	amalgamalg #1	bar	Unknown	1	3284.93	4914.11
+Women	dresses	amalgamalg #2	bar	Unknown	5	878.42	2506.96
+Women	dresses	amalgamalg #1	able	Unknown	5	2891.95	4507.24
+Men	shirts	importoimporto #1	eing	Unknown	7	3465.77	5074.07
+Women	dresses	amalgamalg #2	eing	Unknown	3	964.29	2569.34
+Women	dresses	amalgamalg #1	bar	Unknown	6	3324.49	4914.11
+Men	shirts	importoimporto #1	ought	Unknown	3	3467.09	5045.75
+Women	dresses	amalgamalg #1	ought	Unknown	5	3049.25	4619.70
+Women	dresses	amalgamalg #1	eing	Unknown	3	2978.07	4528.82
+Men	shirts	importoimporto #1	ought	Unknown	4	3544.97	5045.75
+Men	shirts	importoimporto #1	bar	Unknown	1	3490.62	4973.50
+Men	shirts	importoimporto #2	able	Unknown	2	628.63	2095.47
+Men	shirts	importoimporto #1	eing	Unknown	1	3628.21	5074.07
+Women	dresses	amalgamalg #1	able	Unknown	4	3082.17	4507.24
+Men	shirts	importoimporto #2	eing	Unknown	5	765.86	2137.65
+Women	dresses	amalgamalg #2	ation	Unknown	4	1064.70	2431.22
+Women	dresses	amalgamalg #2	ought	Unknown	3	991.89	2355.44
+Women	dresses	amalgamalg #2	eing	Unknown	5	1211.13	2569.34
+Women	dresses	amalgamalg #2	ation	Unknown	7	1078.41	2431.22
+Men	shirts	importoimporto #2	ation	Unknown	4	807.67	2141.06
+Women	dresses	amalgamalg #2	ation	Unknown	2	1102.89	2431.22
+Women	dresses	amalgamalg #2	eing	Unknown	6	1247.86	2569.34
+Women	dresses	amalgamalg #2	able	Unknown	4	1020.39	2337.27
+Men	shirts	importoimporto #2	able	Unknown	7	780.91	2095.47
+Women	dresses	amalgamalg #2	ese	Unknown	2	904.34	2200.82
+Women	dresses	amalgamalg #2	ought	Unknown	7	1061.87	2355.44
+Women	dresses	amalgamalg #2	able	Unknown	3	1054.94	2337.27
+Men	shirts	importoimporto #2	ought	Unknown	6	633.96	1908.05
+Men	shirts	importoimporto #2	ese	Unknown	6	853.37	2113.57
+Women	dresses	amalgamalg #2	able	Unknown	5	1084.94	2337.27
+Women	dresses	amalgamalg #2	eing	Unknown	4	1317.08	2569.34
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q90.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q90.out
new file mode 100644
index 0000000000..3d25748a46
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q90.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q90 --
+0.6124401913875598
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q91.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q91.out
new file mode 100644
index 0000000000..486b83955e
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q91.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q91 --
+AAAAAAAACAAAAAAA	Mid Atlantic	Felipe Perkins	109.74
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q92.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q92.out
new file mode 100644
index 0000000000..6dc1184503
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q92.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q92 --
+39529.71
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q93.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q93.out
new file mode 100644
index 0000000000..eaea2a5a49
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q93.out
@@ -0,0 +1,103 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q93 --
+213	\N
+287	\N
+458	\N
+1756	\N
+1990	\N
+2181	\N
+2571	\N
+2660	\N
+3640	\N
+4093	\N
+4572	\N
+4694	\N
+4842	\N
+4932	\N
+5667	\N
+6154	\N
+6437	\N
+6811	\N
+6948	\N
+7077	\N
+7476	\N
+7857	\N
+8694	\N
+9050	\N
+9125	\N
+9379	\N
+10225	\N
+11285	\N
+11603	\N
+11907	\N
+12721	\N
+13182	\N
+13405	\N
+14302	\N
+15425	\N
+15690	\N
+15698	\N
+16080	\N
+16348	\N
+16563	\N
+16701	\N
+16917	\N
+17223	\N
+17229	\N
+17282	\N
+17682	\N
+17759	\N
+17897	\N
+17910	\N
+18453	\N
+18747	\N
+19219	\N
+19915	\N
+20161	\N
+20214	\N
+20528	\N
+21523	\N
+21734	\N
+21853	\N
+22206	\N
+23080	\N
+23469	\N
+23557	\N
+23748	\N
+24187	\N
+24652	\N
+25029	\N
+25169	\N
+25757	\N
+25813	\N
+26587	\N
+26842	\N
+26872	\N
+27004	\N
+27540	\N
+27910	\N
+27977	\N
+28290	\N
+28325	\N
+28676	\N
+30145	\N
+31163	\N
+32195	\N
+32306	\N
+32967	\N
+33062	\N
+34398	\N
+34857	\N
+35264	\N
+35733	\N
+35859	\N
+36260	\N
+36474	\N
+36488	\N
+37231	\N
+37519	\N
+37720	\N
+37770	\N
+37909	\N
+37936	\N
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q94.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q94.out
new file mode 100644
index 0000000000..bf80d5b460
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q94.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q94 --
+33	64554.35	-3979.35
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q95.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q95.out
new file mode 100644
index 0000000000..98bbac9547
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q95.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q95 --
+73	120440.34	42133.12
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q96.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q96.out
new file mode 100644
index 0000000000..d06e979dae
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q96.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q96 --
+870
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q97.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q97.out
new file mode 100644
index 0000000000..8f9450647f
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q97.out
@@ -0,0 +1,4 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q97 --
+540401	286628	174
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q98.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q98.out
new file mode 100644
index 0000000000..e1893f92bd
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q98.out
@@ -0,0 +1,2519 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q98 --
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+AAAAAAAAPEFEAAAA	Sure, important children see almost net, silve	Sports	fitness	4.08	5909.24	1.93
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+AAAAAAAAAJBEAAAA	Relevant, distinctive years speak. Fac	Sports	football	0.42	2341.90	1.01
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+AAAAAAAAEKIDAAAA	Simple, other concentrations must believe indian, common years. Only statistical standards must sort thus lists. Liberal sign	Sports	football	84.88	11883.97	5.15
+AAAAAAAAELHDAAAA	Much leading demonstrations might end once more institutional doubts. Accused authorities should make. Administrative women maintai	Sports	football	3.79	155.70	0.06
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+AAAAAAAAHNJCAAAA	M	Sports	football	2.64	80.16	0.03
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+AAAAAAAAIMECAAAA	Social eyes hear. Important, other fields say ago small, desirable inco	Sports	football	0.70	1612.53	0.69
+AAAAAAAAIODDAAAA	Different days read impossible, old farms. Certain proposals cannot protect long from a pr	Sports	football	5.23	1774.48	0.76
+AAAAAAAAJPCCAAAA	Sources cannot fight as on a names. Years ought to contact well in front of a arms. Prisoners try upwards. Nice, nice drivers vary up to as enormo	Sports	football	1.28	6410.76	2.77
+AAAAAAAALECCAAAA	So overall	Sports	football	4.39	5216.24	2.26
+AAAAAAAALGIDAAAA	Sc	Sports	football	1.08	54.79	0.02
+AAAAAAAALIFBAAAA	Still tough unions must refuse especially services. Authorities play only. Main, nati	Sports	football	6.81	6968.31	3.02
+AAAAAAAAMANAAAAA	Heads fail only serious li	Sports	football	2.40	9890.97	4.28
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+AAAAAAAAMFODAAAA	Enough possible policemen call as racial stairs. Leve	Sports	football	7.89	6699.84	2.90
+AAAAAAAAMIACAAAA	Simple, powerful efforts may like 	Sports	football	4.81	2960.52	1.28
+AAAAAAAAMLMAAAAA	Various, key mines get institutions. 	Sports	football	4.19	4485.29	1.94
+AAAAAAAANLFCAAAA	Suitable fingers would go then new men. Efficient, noble drawings think probably 	Sports	football	4.22	2023.04	0.87
+AAAAAAAANLHDAAAA	Recent communities should not resist political, late relatives. Below essential plans should 	Sports	football	0.76	1495.38	0.64
+AAAAAAAANNKBAAAA	Empty, remarka	Sports	football	9.76	11645.83	5.04
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+AAAAAAAAOHEAAAAA	Absolutely front men turn spatial hours. Good, free sales used to marry outside appropriate ships. Noble men sa	Sports	football	1.83	1.86	0.00
+AAAAAAAAOKEBAAAA	Other organisations imagine often therefore stable blues; horses might grasp things. Talks should not let apparently growing authorities. Factors rescue local objections. Probably wild trustees woul	Sports	football	8.38	3880.28	1.68
+AAAAAAAAOMCBAAAA	Similar men should hope things. Numbers might not opt now organisers. Just false offers determine judges.	Sports	football	2.00	6738.18	2.92
+AAAAAAAAPBDDAAAA	Peaceful adults could attract also	Sports	football	4.69	142.34	0.06
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+AAAAAAAAACCDAAAA	Figures will not wish late primary, sure members. Recently true techniques could bring a little radically economic processes. Distant lips ought to go only civil words. Days claim aback in the kids; 	Sports	golf	4.14	22357.31	5.28
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+AAAAAAAAALECAAAA	Revolutionary investors will not consider often black questions; lines want probably contemp	Sports	golf	1.19	3204.36	0.75
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+AAAAAAAABJPDAAAA	Right competitive tables look devices. Conservative, new cases require low dangers. Quite educational principles assess	Sports	golf	5.22	1569.65	0.37
+AAAAAAAABLNAAAAA	Assets would take. Then great fingers develop en	Sports	golf	7.78	6214.14	1.46
+AAAAAAAABMECAAAA	Over sexual activities should not distinguish so. Really large goals provide to a attitudes; already free arms used to accept even for a days. Black, video-taped names may present both to the	Sports	golf	9.14	6246.87	1.47
+AAAAAAAACAGDAAAA	Friendly, efficient stands forget separately. Lega	Sports	golf	7.38	20385.52	4.81
+AAAAAAAACDIDAAAA	Women could tell still ever mathematical standards	Sports	golf	1.26	7017.24	1.65
+AAAAAAAACFHDAAAA	Crucial, willing styles used to derive in a women. Catholic, other controls sho	Sports	golf	1.49	8639.12	2.04
+AAAAAAAACGCBAAAA	Wonderful, int	Sports	golf	5.94	7497.45	1.77
+AAAAAAAACHAEAAAA	Especially alone payments would mention free, equal eyes. Facilities ought to benefit there occasions. Big meals might prove skills. Chan	Sports	golf	60.91	10605.00	2.50
+AAAAAAAACJIBAAAA	Independent, constant prices smoke; homes might form now accounts. Other ranks could matter again outside the honours. Close, religious methods apply 	Sports	golf	4.55	11903.61	2.81
+AAAAAAAACNPCAAAA	Poor, eventual homes would go all foreign powers. Pupils would find most great laws. Twi	Sports	golf	1.07	2867.53	0.67
+AAAAAAAADICAAAAA	Members become so poor peri	Sports	golf	32.36	4124.04	0.97
+AAAAAAAADLFAAAAA	Also silent nurses find also fully mental priorities. Savings shall bring naturally silent e	Sports	golf	3.04	16051.84	3.79
+AAAAAAAAECFDAAAA	Old others tell; immediate eggs leave terms. Seats involve sensibly anyway royal individuals. Interesting, american year	Sports	golf	3.73	4534.82	1.07
+AAAAAAAAECOAAAAA	Regulations would live parents. Departments shall not want. Standards must not cost difficult authorities. Young, international levels achieve nicely for a participants. Probably busy	Sports	golf	43.29	1105.40	0.26
+AAAAAAAAEDADAAAA	Global actions slip etc windows. Probably true papers know both with a months. Other states let industrial, open lectures. Expressions climb within a doubts. So western details	Sports	golf	3.75	7735.51	1.82
+AAAAAAAAENCEAAAA	Services go certain beans. Away american words lose quickly powerful skills. Certainly physical films would turn rather later central miles; great governments re	Sports	golf	0.71	20947.28	4.94
+AAAAAAAAEPCEAAAA	Results decide hence eventually economic races. American, underlying tourists shall secure too adult sig	Sports	golf	64.31	1080.57	0.25
+AAAAAAAAFANBAAAA	There only decisions take really royal, joint words. Too public copies must not invent so-called, important aspects. Human, positive organisations would view more male phrases. Relations must n	Sports	golf	4.20	3922.85	0.92
+AAAAAAAAFBABAAAA	Experimental users know even extremely small aspects. Regular 	Sports	golf	2.85	14440.52	3.41
+AAAAAAAAFIIBAAAA	Facts finish other passengers. Similar societies live personally. Visitors would manage light, new rocks; parts can brin	Sports	golf	8.20	3304.37	0.78
+AAAAAAAAGAAEAAAA	New, confidential neighbours capture	Sports	golf	3.48	8839.02	2.08
+AAAAAAAAGCCEAAAA	Then narrow problems show now just social competitors. Lives may not become individual, bloody resources; roots 	Sports	golf	1.10	6965.97	1.64
+AAAAAAAAGDEEAAAA	Carefully european characters drop foreigners. Foreign funds wear; silver, empty councils use personally positive, english matters. Servic	Sports	golf	6.37	4816.06	1.13
+AAAAAAAAGEDBAAAA	Systems submit often priests. Publications shall close high friendly instruments. Levels look white countries. Human, close weeks say never civil, small collections. Tory, tr	Sports	golf	8.58	1498.11	0.35
+AAAAAAAAGHAEAAAA	Paintings may market mistakenly dependent occasions; nearly good children might not put now rights. Current services see for a relationships; faces could keep too nearby, diverse p	Sports	golf	7.67	4495.20	1.06
+AAAAAAAAGJMCAAAA	Long-term game	Sports	golf	4.19	20224.07	4.77
+AAAAAAAAGMKDAAAA	Best odd changes used to pass underlying minutes; good others could 	Sports	golf	4.29	16608.35	3.92
+AAAAAAAAHDPAAAAA	Early, possible forces release long dirty 	Sports	golf	6.26	13323.43	3.14
+AAAAAAAAHNHBAAAA	Views should cultivate even ambitious, in	Sports	golf	1.58	2276.99	0.53
+AAAAAAAAIEDEAAAA	Different years complain merely comprehensive, effective weeks. Images will discuss honours; similar centres get now needs. Foreign errors last sepa	Sports	golf	0.85	885.40	0.20
+AAAAAAAAIEEBAAAA	New interests feel home for the experiences. Services call numerous actions; ch	Sports	golf	7.82	2194.72	0.51
+AAAAAAAAIHMCAAAA	Social, identical doubts might	Sports	golf	4.59	10647.05	2.51
+AAAAAAAAIMFDAAAA	Almost major songs afford big characters. International	Sports	golf	3.54	585.78	0.13
+AAAAAAAAIMHBAAAA	British, quick friends might make early good min	Sports	golf	2.17	11931.00	2.81
+AAAAAAAAIPJAAAAA	Countries put away indeed social services.	Sports	golf	9.43	9982.10	2.35
+AAAAAAAAJDEAAAAA	Economic, impressive thoughts will not neglect. Strong, serious records should capture o	Sports	golf	8.11	10722.62	2.53
+AAAAAAAAJIJCAAAA	Skills might swallow together. Also emotional styles should not address on	Sports	golf	8.91	7359.85	1.73
+AAAAAAAAKBJCAAAA	For example physical events shall find far fires; courts reveal poor experiences. Others control to the activities. Square features answ	Sports	golf	2.63	19026.67	4.49
+AAAAAAAAKEPCAAAA	Practical stations admit increasingly. Pr	Sports	golf	1.53	6248.86	1.47
+AAAAAAAAKJEAAAAA	Clearly conservative children could not moderate with a decisions. As good as important track	Sports	golf	7.66	2477.50	0.58
+AAAAAAAAKMMAAAAA	Specific walls go conversely russian women. Correctly fair priorities track to a lives. Complete memorie	Sports	golf	2.22	4258.62	1.00
+AAAAAAAAKPECAAAA	Full, rural artists must not notice deeper historical stages; other years may preserve apparently traditional solicitors. Central, old years will not manage best qu	Sports	golf	1.81	11366.84	2.68
+AAAAAAAAKPPAAAAA	Young hands report. Children would bre	Sports	golf	4.09	665.12	0.15
+AAAAAAAALCBCAAAA	Western elements shall not remember in the unions. Cruel assessments show again important teachers. Later real pp. engage boring hands. Earli	Sports	golf	6.67	397.44	0.09
+AAAAAAAALJOAAAAA	Buildings would not get with a tools. Current, united elections 	Sports	golf	0.82	271.20	0.06
+AAAAAAAALLHCAAAA	Secondary, british forces cou	Sports	golf	3.20	5029.51	1.18
+AAAAAAAAMDKDAAAA	Long only eyes used to accept light, american	Sports	golf	8.72	877.92	0.20
+AAAAAAAAMEECAAAA	Direct records would not marry in a suggestions. External standards avoid nice services. Large secrets	Sports	golf	0.42	4771.19	1.12
+AAAAAAAAMEFCAAAA	Objectives object so remaining, young thousands. Fires need years. Like years shall like either times. Hands demonstrate direct just happy bodies; though arab functions should n	Sports	golf	7.24	3317.80	0.78
+AAAAAAAAMLBCAAAA	Nervous, alt	Sports	golf	9.38	2595.87	0.61
+AAAAAAAANDDDAAAA	Private, extreme books will for	Sports	golf	0.74	4637.54	1.09
+AAAAAAAANEEEAAAA	Even s	Sports	golf	1.45	656.18	0.15
+AAAAAAAANKHBAAAA	Young figures should go onl	Sports	golf	9.27	\N	\N
+AAAAAAAANMJAAAAA	High members may not fulfil by a officials. Bishops may practise well to a bodies; both considerable problems would not make however organic important things. Particular, old companies must take 	Sports	golf	5.84	5794.81	1.36
+AAAAAAAAOGPAAAAA	Well planned problems use more in a origins; main, senior sons enter right, substantial faces. Typical, other measures must counteract so minutes; yet 	Sports	golf	1.28	9198.36	2.17
+AAAAAAAAOLNDAAAA	Senior judges save. Possib	Sports	golf	3.12	4798.50	1.13
+AAAAAAAAPABDAAAA	Hardly historical dollars combine quit	Sports	golf	3.32	263.51	0.06
+AAAAAAAAPJNDAAAA	Terms used to settle with the considerations; final contents would address more old agreements; areas would not get either hard, deaf heads. Successfully standard hours will reconstruct. Events	Sports	golf	1.27	2779.34	0.65
+AAAAAAAAAAEDAAAA	Concerned politicians cannot listen there. Sometimes other followers occur urban, physical years. Concerned words might not set. Workers can perform then in a individuals. So strong im	Sports	guns	3.30	429.26	0.11
+AAAAAAAAABDDAAAA	Rates ought to lead again present variables. Also strong students scream. Exact, dutch feet open; dail	Sports	guns	93.05	678.41	0.17
+AAAAAAAAABGEAAAA	Confident areas would happen without a arguments. Soft mountains allow moderately contempora	Sports	guns	3.23	2405.90	0.63
+AAAAAAAAABHAAAAA	Old sources pull later examples. Rich others ought to e	Sports	guns	6.47	14117.29	3.69
+AAAAAAAAAMDCAAAA	Things keep at a others. Full, central wage	Sports	guns	2.94	12137.48	3.18
+AAAAAAAABKDCAAAA	Wide, certain v	Sports	guns	5.44	505.47	0.13
+AAAAAAAACAKCAAAA	Always complex areas would convince less much local lawyers; modern others can sue home reasonable proposals.	Sports	guns	4.59	11371.34	2.97
+AAAAAAAACDNBAAAA	Rational, sof	Sports	guns	1.64	22707.64	5.95
+AAAAAAAACJGAAAAA	Clear types buy years. Companies used to go already. Stable, general arrangements will accept purely light	Sports	guns	7.02	9657.94	2.53
+AAAAAAAACKABAAAA	Determined roads might lea	Sports	guns	2.31	5344.12	1.40
+AAAAAAAACLDBAAAA	Little poor markets wriggle commonly roughly strategic times. Able securities can handle involuntarily thus other rates; then famous pri	Sports	guns	2.21	1187.63	0.31
+AAAAAAAACLHAAAAA	Huge, private situations ought to back by an marks. Girls can come also local,	Sports	guns	7.03	7246.86	1.89
+AAAAAAAADKLBAAAA	Public, legal languages might get easier easily regular towns. Very different children fulfil virtually tiles. Everyday, fresh numbers look only large, sole companies	Sports	guns	9.11	4695.99	1.23
+AAAAAAAAECICAAAA	Old, n	Sports	guns	1.37	6973.14	1.82
+AAAAAAAAEHCBAAAA	Devices know also so normal waters. Labour times say. Teachers tell 	Sports	guns	0.26	2073.30	0.54
+AAAAAAAAEHKBAAAA	Extensive circumstances consider already russian discussions. Both open problems try in an charts; wa	Sports	guns	6.89	15948.99	4.17
+AAAAAAAAEPFBAAAA	Seats ought to consult tools. Far strong hundreds include children. Concessions sho	Sports	guns	8.96	8159.48	2.13
+AAAAAAAAFLDDAAAA	Guilty, painful families shall separate inadequate, causal feet. Other, dangerous indians boost efficiently like a children. Aggressi	Sports	guns	14.96	14127.44	3.70
+AAAAAAAAGBFBAAAA	Free pp. think rather to the shoulders. Original rates wil	Sports	guns	3.71	535.60	0.14
+AAAAAAAAGFLDAAAA	Actually other thoughts hold to a places. So only services affect blind, content measures. Formal, other differences would complain open annual, rich methods. Risks acknowledge long; ways 	Sports	guns	4.62	1508.24	0.39
+AAAAAAAAGMEDAAAA	Blind, real systems may not intervene even later real standards. Unnecessarily other others might clarify in a doors. Here catholic manager	Sports	guns	3.81	11675.92	3.05
+AAAAAAAAGODDAAAA	Traditional, necessary activities would get thick safely aware demands. Annual, military arrangement	Sports	guns	4.44	6448.74	1.68
+AAAAAAAAHOGBAAAA	Standards may open both op	Sports	guns	2.90	24366.68	6.38
+AAAAAAAAIDDCAAAA	New, difficult writings should arrange too never social years. Fresh seasons can stand. Full accountants reserve as the words. Good, public facts see. Inadequate, marin	Sports	guns	4.77	5186.43	1.35
+AAAAAAAAILBDAAAA	Financial, italian wages kno	Sports	guns	5.30	7381.49	1.93
+AAAAAAAAIMMCAAAA	Rows cannot give then magnetic children. Children join again very labour neighbours. Ways shoot. Horses could prepare little to a heels. Residential, stable issues disappear automaticall	Sports	guns	31.00	8425.76	2.20
+AAAAAAAAINFDAAAA	New eyes change political, new activities.	Sports	guns	9.10	11138.94	2.91
+AAAAAAAAJOODAAAA	Likely personnel will not produce in an guidelines; freely tory sanctions give most pp.. Cases may let never players. Appropriate,	Sports	guns	3.77	173.24	0.04
+AAAAAAAAKBHCAAAA	New, british politicians fail particularly in a things. Personal books get; as political nig	Sports	guns	1.17	13290.11	3.48
+AAAAAAAAKFLDAAAA	Days must appear kindly familiar hands. Too negative systems cannot skip existi	Sports	guns	3.00	8788.60	2.30
+AAAAAAAALFOBAAAA	About british reasons will draw occasionally practitioners. New attempts shall display in private private, major magazines. Questions dare on a losses. As american children take upwards good symptom	Sports	guns	72.70	6798.49	1.78
+AAAAAAAALLNCAAAA	Again integrated circumstances used to remove especially about	Sports	guns	1.13	552.75	0.14
+AAAAAAAAMEHAAAAA	So married arts must not land somewhat. Specific, long cases cover today existing, southern reasons; well substantial features would not sell b	Sports	guns	0.86	2072.90	0.54
+AAAAAAAAMEJAAAAA	Sure persons say quicker public, late cells. New, central visitors should not destroy both skills. Circumstances s	Sports	guns	95.42	11171.94	2.92
+AAAAAAAAMFHCAAAA	Eventually effective leads see grey brothers. Others show both for no sorts. Authoriti	Sports	guns	8.46	14552.42	3.81
+AAAAAAAAMIEBAAAA	Shy, young areas would return indeed obvious entries. Following, major villages require for the circumstances. Accordingly safe minutes specify. Serious	Sports	guns	5.29	18218.86	4.77
+AAAAAAAAMJNAAAAA	Ways ought to use so armed, straight operators; inc, only techniques must distinguish never usual authorities. Moral projects show however. Goods will take new, physical cultures. Sufficient	Sports	guns	9.15	4790.32	1.25
+AAAAAAAAMKPDAAAA	High sons must sign home expensive games; boats hit hardly. Customers judge today recent, main gods. Then tory organisations describe also partners. Otherwise jo	Sports	guns	6.69	506.92	0.13
+AAAAAAAAMPNDAAAA	Over important allowances recommend present charges; at least philosophical equations cannot attract please steps. More early sides look permanent years. Low, civil events try also at a theori	Sports	guns	7.59	176.40	0.04
+AAAAAAAANHODAAAA	Suppliers produce to a hours. Special, main factors will come. Old, individual recommendations see 	Sports	guns	30.34	3863.70	1.01
+AAAAAAAANKGCAAAA	Detailed, cognitive friends go less so domestic terms. Again accurate children would break	Sports	guns	7.44	4868.20	1.27
+AAAAAAAANLHAAAAA	Heads might use deeper before a men. Liberal, major authorities must pay extremely broad owners. 	Sports	guns	0.12	4684.24	1.22
+AAAAAAAANODBAAAA	Furthermore low parents used to reach. Young years can rest completely busy woods. Formal, inadequ	Sports	guns	2.17	4753.98	1.24
+AAAAAAAANOHDAAAA	Al	Sports	guns	4.59	6630.42	1.73
+AAAAAAAAOBLDAAAA	Unable pairs must think more successfully nearby families. Fed	Sports	guns	9.08	5127.45	1.34
+AAAAAAAAOENDAAAA	Cle	Sports	guns	9.82	7032.34	1.84
+AAAAAAAAOMDCAAAA	New, low companies arrange times. Available, foreign troops can complain usuall	Sports	guns	80.57	92.26	0.02
+AAAAAAAAOODAAAAA	Above ships can upset before public children; however sharp consumers may not see great pounds. Environme	Sports	guns	6.00	87.32	0.02
+AAAAAAAAOOGBAAAA	Confident teeth give natural, dark directions. Complete, english members shall feel most. Then generous pp. 	Sports	guns	36.92	20209.36	5.29
+AAAAAAAAPANDAAAA	Efficiently political examples can abandon very severe facilities; extraordinary, international hours shall restore at all part-time, following goods. Sports 	Sports	guns	5.61	10197.52	2.67
+AAAAAAAAPCDCAAAA	Front words must not develop societies. Eventual, grey countries make strangely times; ever old indicators send often tomorrow prime computers. Full, high days will come unique companies. Of course s	Sports	guns	4.39	9467.29	2.48
+AAAAAAAAPCHBAAAA	Strong memb	Sports	guns	6.63	804.38	0.21
+AAAAAAAAPLOAAAAA	Regional sets may call then much social securities; gentlemen must launch so further national women.	Sports	guns	2.46	6287.03	1.64
+AAAAAAAAABCEAAAA	Other, recent representations resolve both normal talks. Old, unlikely specialists apply just complete cl	Sports	hockey	5.17	3748.04	1.87
+AAAAAAAAAEKCAAAA	Ordinary metals would transport with a policies; about arbitrary balls must go sites. Clear prices continue of course. I	Sports	hockey	54.72	397.06	0.19
+AAAAAAAAAENCAAAA	Glad heads answer more perhaps large risks. Imaginative guests a	Sports	hockey	1.55	887.66	0.44
+AAAAAAAAAKJDAAAA	Strong, mass owners would upset followers. All vital colleagues shall remember whole police. Alive, horrible explanations should not earn. Then social 	Sports	hockey	0.98	2912.58	1.45
+AAAAAAAABDPDAAAA	Services indicate feature	Sports	hockey	2.41	3535.46	1.77
+AAAAAAAABJPAAAAA	Soon intermediate needs should increase more feet. Useful participants enable; much 	Sports	hockey	77.28	9672.60	4.84
+AAAAAAAABMJAAAAA	Other, tight solicitors shall not win now clouds. There base drugs contain well by a workers; local churches expect usually applications; more open creditors should not improve even. The	Sports	hockey	2.66	1377.88	0.69
+AAAAAAAACANBAAAA	Months cannot lead never unlikely problems. Special characteristics ought to borrow over banks. Patients make only. Networks might not want things. At least bad qualities would not gi	Sports	hockey	4.71	3405.42	1.70
+AAAAAAAACHBAAAAA	Persons would not tell talks; no doubt internal criteria see totally t	Sports	hockey	2.13	1763.28	0.88
+AAAAAAAACLCCAAAA	Complex sports satisfy as. Backwards whole women could give suddenly at a bod	Sports	hockey	94.58	2132.81	1.06
+AAAAAAAACLJAAAAA	Institutions help shel	Sports	hockey	3.69	2344.11	1.17
+AAAAAAAACMKBAAAA	Previous, unusual pounds could concentrate short by the articles. For example possible	Sports	hockey	8.04	2849.49	1.42
+AAAAAAAADEDDAAAA	Original, everyday words may not wish even to a paintings. Domestic movements could explore on a improvements. For example specialist contracts use as more subtle weekends. Annual, good performanc	Sports	hockey	5.19	4481.04	2.24
+AAAAAAAADLCAAAAA	Recent, french conservatives cannot get somehow; decisions save accordingly happy thousands. Seriously good years argue then golden attacks. Just wide eyes drink underground likely, fin	Sports	hockey	0.09	1868.24	0.93
+AAAAAAAADLODAAAA	Words would hear successfully unhappily external restaurants. Things must get also ready instruments. Heavy, liberal women learn just general matches. Loudly subjective schools will disturb as	Sports	hockey	7.94	4216.76	2.11
+AAAAAAAAEEADAAAA	Long-term cigarettes ensure because of a commentators; days run per a reports; bodies include there in a rocks. Necessary privileges should resist alre	Sports	hockey	13.77	2994.70	1.50
+AAAAAAAAEMFEAAAA	Classes clean best public, fresh subjects. Eyes define both in the moves. Twice physical substances lunch earlier; advanced, simple cases depend else individual, single e	Sports	hockey	4.56	10788.94	5.40
+AAAAAAAAFICBAAAA	Inevitable, local risks emphasize c	Sports	hockey	3.52	7596.53	3.80
+AAAAAAAAFMBEAAAA	Local, final users must not make below; thus significant deputies find widely by the affairs. Anonymous, british instruments enter almost written, expensive shareholders.	Sports	hockey	7.88	1140.10	0.57
+AAAAAAAAGGEDAAAA	Fairly national methods could lead only yards. Crucial, personal sto	Sports	hockey	0.32	9994.86	5.00
+AAAAAAAAGIFEAAAA	Northern, sure arts 	Sports	hockey	5.33	3176.79	1.59
+AAAAAAAAGIMDAAAA	Never precise needs meet never mothers. Po	Sports	hockey	1.34	4503.87	2.25
+AAAAAAAAGOIAAAAA	Human, cons	Sports	hockey	0.45	6322.86	3.16
+AAAAAAAAHADDAAAA	Things wo	Sports	hockey	5.04	1494.08	0.74
+AAAAAAAAHDJAAAAA	Deeply human resources ought to tackle fam	Sports	hockey	3.78	7620.13	3.81
+AAAAAAAAHDOBAAAA	Rights will try useful, intermediate thousands. Main aspirations keep there bright, possible lives. Problems render however significant, strange func	Sports	hockey	5.08	1207.08	0.60
+AAAAAAAAHLEAAAAA	Serious, social teams could not take also other, blind designers. Clear groups would find ot	Sports	hockey	7.00	19425.53	9.73
+AAAAAAAAIHHBAAAA	Just agricultural years may not talk. Superior, national units will not understand now looks; fresh, soft values trust. Partners ought to discredit methods. Gothic, 	Sports	hockey	8.39	1168.00	0.58
+AAAAAAAAIIADAAAA	Elements mention faintly free railways. Pe	Sports	hockey	3.00	3492.34	1.75
+AAAAAAAAIPNBAAAA	Different shares shall last even words. Contracts make on a others. Far from awful colleagues know right years. Names know in a letters. High varieties ought to undergo successful, immed	Sports	hockey	8.97	11904.54	5.96
+AAAAAAAAKNBBAAAA	Friends send central, canadian beds. Wholly new organisations save thus heads. Complete students will com	Sports	hockey	4.68	3706.65	1.85
+AAAAAAAALEEAAAAA	Terms cannot enc	Sports	hockey	5.90	182.31	0.09
+AAAAAAAAMHDBAAAA	Colleges may know closer in a seeds. Conditions fail higher dangerous fears. Changes answer. Selective, sad weeks can register just circumstances. Today gastric publishers can get by a procedures. 	Sports	hockey	9.05	8338.04	4.17
+AAAAAAAAMKAAAAAA	Unacceptable, widespread towns may not block there about a records. Then 	Sports	hockey	0.83	4173.83	2.09
+AAAAAAAAMKHDAAAA	As well lexical teams identify to a points; large times star	Sports	hockey	4.08	12700.97	6.36
+AAAAAAAANFICAAAA	Yet only months can repeat reader	Sports	hockey	1.82	3106.80	1.55
+AAAAAAAANMIBAAAA	Exotic rights could not commit here persistent 	Sports	hockey	3.07	1880.28	0.94
+AAAAAAAAOAAAAAAA	Teachers carry by the children; old democrats enco	Sports	hockey	1.85	1481.72	0.74
+AAAAAAAAOCICAAAA	Otherwise political systems know surely unable	Sports	hockey	4.94	4411.00	2.21
+AAAAAAAAOFIBAAAA	Shallow, vocational efforts used to give very part-time programmes. Only months ought to know; participants will not take then even natural events. Influences take al	Sports	hockey	7.44	2694.77	1.35
+AAAAAAAAOJCBAAAA	Traditional, small 	Sports	hockey	2.31	4850.82	2.43
+AAAAAAAAPMPBAAAA	Good patients used to work then valuable, public rights; current schools shall not complain. Pounds go probably losses; exercises should pray markedly in the materials. New, good players reac	Sports	hockey	3.41	13606.55	6.81
+AAAAAAAAAADCAAAA	Whole reports will not acquire; looks get then japanese, basic creditors. New, fortunate professionals encourage firmly rich roles; however secondary projects might 	Sports	optics	2.72	6010.93	1.74
+AAAAAAAAAEAAAAAA	Both new conditions ask acute, ashamed pupils. Short, poor fami	Sports	optics	2.02	9291.26	2.69
+AAAAAAAAAEMAAAAA	Results should search so middle, jewish services. Ago long points shall use usually various stores. Possible, old polls recover initially contracts; all medical parents join then negative pages	Sports	optics	1.16	5866.20	1.70
+AAAAAAAAAFPAAAAA	Miles could produce probably seconds; small officials will build islands. Natural specialists s	Sports	optics	8.45	3472.88	1.00
+AAAAAAAAAGFDAAAA	Warm, welsh attitudes start over agricultural, eng	Sports	optics	4.07	8830.74	2.55
+AAAAAAAAAKGAAAAA	Entries close only busy objects; involved, grateful refugees stand sui	Sports	optics	1.73	9583.66	2.77
+AAAAAAAAAMHAAAAA	Social, reduced rebels would not achieve very free ships. Selective	Sports	optics	3.41	6250.02	1.81
+AAAAAAAAAMOAAAAA	Follow	Sports	optics	9.98	5054.82	1.46
+AAAAAAAABFCCAAAA	Endless, professional others create by a years; large vis	Sports	optics	1.24	8439.95	2.44
+AAAAAAAABIABAAAA	Children ma	Sports	optics	6.80	4282.62	1.24
+AAAAAAAABKCAAAAA	Of course heavy persons get now implications. Phases show even. So old women develop; big, other jeans drive frantically official shots. Facts might disturb too new, gentle children. G	Sports	optics	0.79	959.95	0.27
+AAAAAAAABKIDAAAA	Leaves go most parties. Available, rich masses appear as administrative feet. Times could not explore at a chairs. Assistant, clear prices emerge neve	Sports	optics	4.92	84.96	0.02
+AAAAAAAACBHAAAAA	Extra, lesser arms formulate as deaths. Important, 	Sports	optics	2.15	1274.88	0.36
+AAAAAAAACCGAAAAA	Large assets trust even; individuals record formal, short t	Sports	optics	7.78	2743.29	0.79
+AAAAAAAACDBBAAAA	Commercial, radical tenants ought to go once on a methods. Upper	Sports	optics	0.51	8812.06	2.55
+AAAAAAAACFABAAAA	Fine, living women wait aside with the patients. Rarely arbitrary books should know already. Expenses will consider vigorously reports. Houses get there particular, local institutions. Really certain	Sports	optics	7.88	5693.93	1.65
+AAAAAAAACHNDAAAA	Western activiti	Sports	optics	6.61	4812.45	1.39
+AAAAAAAACIFBAAAA	Free proced	Sports	optics	5.97	8583.18	2.48
+AAAAAAAACJGDAAAA	Eyes must like over. Shows will not preserve never active eyes; toxic, complete injuries win howe	Sports	optics	0.80	7906.00	2.29
+AAAAAAAADFFAAAAA	Necessary, social bedrooms think so full poles; babies prove now. Profitable payments used to break there. Major, radical households 	Sports	optics	1.51	12616.99	3.65
+AAAAAAAADMBDAAAA	Social, other resources may know reasonable, distant weeks. New, unexpected rates mean. White, electric generations carry together other t	Sports	optics	3.91	4411.67	1.27
+AAAAAAAADOMCAAAA	Main pupils could expel followers. Sometimes severe horses should keep largely earnings. Years put recently permanent inst	Sports	optics	9.17	1401.30	0.40
+AAAAAAAAEABDAAAA	Clearly short talks disentangle especially with a systems. Frequently new sides could honour actually wrong personal attempts. Estimated needs ought to think highly	Sports	optics	3.04	4.07	0.00
+AAAAAAAAEKBBAAAA	Funds wander months. Advisory forms meet finally; complaints shall please to a roads. Often presen	Sports	optics	3.58	3947.19	1.14
+AAAAAAAAEPEDAAAA	Below new weapons note small, good sections. Later new forms may strike years. Isolated, able critics use all but. Forces will not take then little records; windo	Sports	optics	2.75	1374.45	0.39
+AAAAAAAAFNNAAAAA	Inland branches shall provide only available plants. Now available faces answer. Minutes could offer with a others. Forth bizarre dangers search welcome, b	Sports	optics	1.86	2828.94	0.81
+AAAAAAAAGBOCAAAA	Likely, elected reasons keep. Parents step mainl	Sports	optics	4.40	3922.89	1.13
+AAAAAAAAGGPCAAAA	Capital agencies effect significant parents. Types ask illegal, small events. Deep, great reactions give arrangem	Sports	optics	2.99	9863.24	2.85
+AAAAAAAAGJDEAAAA	Heavily positive heroes seem far exciting values; letters might ask still about a r	Sports	optics	1.66	12566.33	3.64
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+AAAAAAAAKNKBAAAA	Of course large structures describe. Used factors would know commercial benefits. Then appropriate circumstances should not know so new terms; ev	Sports	sailing	2.18	3899.16	1.50
+AAAAAAAAKOAEAAAA	Small, dead particles set recently other boxes. Bright, personal locations house novel jobs. Twice residential judges underpin directions. Others want. Other songs star too p	Sports	sailing	0.78	1941.55	0.75
+AAAAAAAAMAKAAAAA	However important children could expect sincerely by way of a potatoes. Even able cars suggest by the issues. Shoes would perform sincerely 	Sports	sailing	4.86	4448.31	1.71
+AAAAAAAAMCJCAAAA	Exactly left yea	Sports	sailing	0.54	6631.39	2.56
+AAAAAAAAMECCAAAA	Desirable stars should introduce to	Sports	sailing	6.99	5638.06	2.17
+AAAAAAAANAIBAAAA	Fond sentences must add in a documents. Also in	Sports	sailing	11.59	6231.21	2.40
+AAAAAAAANCPBAAAA	Average, mean unions include. Cold ways shall work particularly from no rights. Already crucial agencies get very professional days. Perhaps huge methods rule financially awful arms. Strong vehicl	Sports	sailing	7.97	4916.04	1.90
+AAAAAAAANMMDAAAA	Friends used to assume otherwise; interested days take days. A bit primary exports should break steadily serious modern responsibilities. Judges can provide as american, mysterious schools.	Sports	sailing	1.52	28193.51	10.90
+AAAAAAAAOACDAAAA	Men break for the magistrates. Eager, bad forms must not support very famous things; go	Sports	sailing	4.67	4159.07	1.60
+AAAAAAAAOADCAAAA	Facilities increase. Economic holders see ancient animals. Little e	Sports	sailing	0.98	2137.13	0.82
+AAAAAAAAOCDEAAAA	Electrical, warm buildings die; more poor hopes must monitor never evident patients. Heavy issues would identify real, british armies; big, enormous claims lie yet home 	Sports	sailing	5.78	729.17	0.28
+AAAAAAAAODLDAAAA	Tasks can vote only basic men. Profits should not check later everyday decades. Favorite hands	Sports	sailing	7.47	3762.20	1.45
+AAAAAAAAOIKAAAAA	Great, old things will back about however modern yards. Rather selective rows may not try presumably differences. Weapons used to read organizations; go	Sports	sailing	4.36	2630.35	1.01
+AAAAAAAAPCBBAAAA	Social, resulting branches mi	Sports	sailing	7.52	5343.12	2.06
+AAAAAAAAPEFBAAAA	Tears present total duties. Minutes may not m	Sports	sailing	5.27	1803.00	0.69
+AAAAAAAAPKCBAAAA	Growing, different minutes agree actually in accordance with a units. Necessary powers make even. Brown, high names would not say; sales must no	Sports	sailing	1.22	8285.78	3.20
+AAAAAAAAPKMDAAAA	Panels ought to make relations. Adverse, new calculations mu	Sports	sailing	3.69	2543.06	0.98
+AAAAAAAAADIAAAAA	Lips see outside quickly protective systems. 	Sports	tennis	4.65	8227.57	2.83
+AAAAAAAAAEAEAAAA	Men shall not play so financial shares; just black deposits might say probably. Level exhibitions receive safely empty, international investors. Industri	Sports	tennis	27.60	7679.09	2.64
+AAAAAAAAAEHCAAAA	Quite social police choose. Recent, old lives go in a voices. Inherent, busy competitors ought to win local, basic titles. However ready years need m	Sports	tennis	1.71	12612.57	4.35
+AAAAAAAAAILAAAAA	Hands respond quickly heavy armies. Firms must reduce into a numbers; personal, british figures transfer entirely logi	Sports	tennis	3.17	2894.28	0.99
+AAAAAAAAAKECAAAA	Importantly differen	Sports	tennis	7.92	10177.21	3.51
+AAAAAAAAAODCAAAA	Well major enemies might access only extra good parties. Other, quiet eyes can buy completely western, effective feelings; materi	Sports	tennis	3.89	15012.51	5.17
+AAAAAAAAAPOAAAAA	A little average flames ought to break old, unique men. Things select often red, economic others. Hands will lift sufficiently; german, proper sections worry perhaps for the po	Sports	tennis	1.79	25290.31	8.72
+AAAAAAAABMNCAAAA	Low, fair hours lead other stones. Also clear differences mention eastern contexts; men end essential, ltd. ages. International, cultural months continue earlier. Problems reduce	Sports	tennis	2.90	4504.82	1.55
+AAAAAAAACCABAAAA	Alone rises mus	Sports	tennis	1.09	2876.08	0.99
+AAAAAAAACCAEAAAA	Top costs ask less real husbands. Cautious, other tactics catch. Talks will not steal now. Stages use; massive changes get even with the l	Sports	tennis	3.12	18361.88	6.33
+AAAAAAAACGBEAAAA	Right weeks might rain further satisfactorily valuable hospitals. Yellow years could create so large, right changes. Rows must spend only.	Sports	tennis	0.97	6908.74	2.38
+AAAAAAAACGOBAAAA	Awkward, poor points cannot weigh plants. Single, reasonable players may not go around scottish products. Then presidential years suffer clubs. Problems would attrac	Sports	tennis	4.15	10926.00	3.76
+AAAAAAAACICCAAAA	Other, other changes used to sort light facts. Issues help fully usual, fair gr	Sports	tennis	2.25	8608.85	2.96
+AAAAAAAACJCBAAAA	English activities explain old principles. Years make other, little governors; able materials shrink grimly by the wishes. Wide months prevent so in a adults. Functions cannot ask blind events. St	Sports	tennis	1.00	5962.12	2.05
+AAAAAAAACJFEAAAA	Molecular eyes turn different terms. Details will attack large, implicit members. Acceptable, only drugs br	Sports	tennis	2.95	11254.12	3.88
+AAAAAAAACMFBAAAA	Museums addre	Sports	tennis	5.20	15262.13	5.26
+AAAAAAAADHCEAAAA	Alone, international clients can retire at least other services; even major properties come in a grounds.	Sports	tennis	68.55	6569.13	2.26
+AAAAAAAAEFFCAAAA	Animals cannot make most sides; just wealthy babies could fulfil as before a records. Now literary results used to say human, unique genes. Bo	Sports	tennis	4.85	1131.00	0.39
+AAAAAAAAEKIAAAAA	Unlikely letters inhibit only jobs. Brightly hard procedures might eat mainly complex odd tories. Powers would not achieve too dem	Sports	tennis	2.51	5191.75	1.79
+AAAAAAAAEPHCAAAA	Equally adequate schools obtain for a commentators. Women would keep suddenly systems. Disastrous, old authorities enforc	Sports	tennis	0.23	942.98	0.32
+AAAAAAAAFEMBAAAA	Natural hands will see almost simple, alone seconds. Regulations shall impress white,	Sports	tennis	99.85	3415.62	1.17
+AAAAAAAAFHNDAAAA	Machines cannot fit too successive levels. Inner, european eyes could call now misleading,	Sports	tennis	4.86	6685.68	2.30
+AAAAAAAAGGFDAAAA	Bad, various p	Sports	tennis	8.16	10783.34	3.71
+AAAAAAAAGNHDAAAA	Economic standards shall bring even strong measures. More main improvements want 	Sports	tennis	4.72	216.30	0.07
+AAAAAAAAHJOBAAAA	Highly local li	Sports	tennis	9.81	16310.70	5.62
+AAAAAAAAIFFCAAAA	Most neat years must pitch with a minutes. Quite symbolic accounts should not engage never either normal girls. Somehow specific s	Sports	tennis	3.56	1278.99	0.44
+AAAAAAAAINDEAAAA	Sexual, green processes enjoy so single, vast advisers. Recently c	Sports	tennis	2.61	7287.48	2.51
+AAAAAAAAIPKBAAAA	Fine minds would not ask usually securities. Immediate, natural classes come personally angles. White years shall appear important, material aspects; simply general years organize al	Sports	tennis	5.66	908.15	0.31
+AAAAAAAAKDCEAAAA	Big, huge goals add usually here commercial things; keen, pregnant years might imagine somewhere rules. Highly respo	Sports	tennis	2.11	\N	\N
+AAAAAAAAKHEEAAAA	Active values may not capture. Casually political minutes would recognis	Sports	tennis	2.20	1466.29	0.50
+AAAAAAAAKKCEAAAA		Sports	tennis	\N	3075.00	1.06
+AAAAAAAAKLDEAAAA	Difficult, adult details can know exactly western, other problems. Closed activities might serve easy, open cases. Numbers end even even busy jobs. Social, wrong eggs play of course with a figure	Sports	tennis	1.10	2962.43	1.02
+AAAAAAAALFJDAAAA	Friendly offices feel. Delightful servants give almost previously natural earnings. Written, important books press subject, american parents. New, reduced days shall n	Sports	tennis	0.40	4498.59	1.55
+AAAAAAAALOHCAAAA	Other, clinical senses display more. Suddenly video-taped friends take here local, african policies. Muscles think much local letters. Tired, parti	Sports	tennis	2.50	4619.48	1.59
+AAAAAAAAMCBCAAAA	American, far marks consider early comments. Carefully various recordings see brief patients; hours bring local calls. Often various scenes capitalise coming, other a	Sports	tennis	53.43	10911.68	3.76
+AAAAAAAANCKAAAAA	Green, different animals might delay mostly other, similar miles. Then tiny attempts take obviously very constant machines. Prime schools like again pe	Sports	tennis	4.58	6298.64	2.17
+AAAAAAAANFOCAAAA	Active, red things shall remain from the colleagues; largely high members form barely i	Sports	tennis	5.94	275.45	0.09
+AAAAAAAANNBEAAAA	Possible, friendly goods slow certainly prepared, obviou	Sports	tennis	0.69	3601.94	1.24
+AAAAAAAANPPDAAAA	Top goals set private things. Too strange years reduce especially national differe	Sports	tennis	3.95	1370.84	0.47
+AAAAAAAAOAMAAAAA	Professional interests cannot accept necessarily. Settlements cook cheap h	Sports	tennis	1.98	780.00	0.26
+AAAAAAAAOCMBAAAA	Others navigate projects. Democratic, experimental margins ought to tell often personal, current reasons. Ph	Sports	tennis	17.35	7175.61	2.47
+AAAAAAAAOKHAAAAA	So british cases could not know hard. Grateful, single drugs should not get secondly international levels. Considerations used to connect governments. Exact men get at a patients. Yesterday good men s	Sports	tennis	19.51	10576.76	3.64
+AAAAAAAAOPGDAAAA	Households help minutes. C	Sports	tennis	2.37	3171.34	1.09
+AAAAAAAAPBMDAAAA	Superior contributions speed. Areas should en	Sports	tennis	95.22	1843.31	0.63
+
diff --git a/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q99.out b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q99.out
new file mode 100644
index 0000000000..a0a5ea3ccb
--- /dev/null
+++ b/regression-test/data/datev2/tpcds_sf1_p1/sql/pipeline_q99.out
@@ -0,0 +1,93 @@
+-- This file is automatically generated. You should know what you did if you want to edit this
+-- !pipeline_q99 --
+	EXPRESS	Mid Atlantic	1223	1314	1257	0	0
+	EXPRESS	NY Metro	1274	1296	1286	0	0
+	EXPRESS	North Midwest	1159	1329	1276	0	0
+	LIBRARY	Mid Atlantic	941	978	948	0	0
+	LIBRARY	NY Metro	886	1001	1009	0	0
+	LIBRARY	North Midwest	917	943	991	0	0
+	NEXT DAY	Mid Atlantic	1304	1264	1349	0	0
+	NEXT DAY	NY Metro	1271	1251	1291	0	0
+	NEXT DAY	North Midwest	1199	1233	1273	0	0
+	OVERNIGHT	Mid Atlantic	965	989	967	0	0
+	OVERNIGHT	NY Metro	979	993	1039	0	0
+	OVERNIGHT	North Midwest	946	1016	905	0	0
+	REGULAR	Mid Atlantic	933	994	997	0	0
+	REGULAR	NY Metro	961	1022	1034	0	0
+	REGULAR	North Midwest	893	921	949	0	0
+	TWO DAY	Mid Atlantic	972	968	972	0	0
+	TWO DAY	NY Metro	926	974	1004	0	0
+	TWO DAY	North Midwest	941	921	981	0	0
+Bad cards must make.	EXPRESS	Mid Atlantic	1270	1318	1281	0	0
+Bad cards must make.	EXPRESS	NY Metro	1226	1287	1282	0	0
+Bad cards must make.	EXPRESS	North Midwest	1208	1242	1294	0	0
+Bad cards must make.	LIBRARY	Mid Atlantic	962	976	1009	0	0
+Bad cards must make.	LIBRARY	NY Metro	978	984	999	0	0
+Bad cards must make.	LIBRARY	North Midwest	898	959	958	0	0
+Bad cards must make.	NEXT DAY	Mid Atlantic	1225	1328	1327	0	0
+Bad cards must make.	NEXT DAY	NY Metro	1262	1325	1246	0	0
+Bad cards must make.	NEXT DAY	North Midwest	1227	1300	1276	0	0
+Bad cards must make.	OVERNIGHT	Mid Atlantic	956	935	990	0	0
+Bad cards must make.	OVERNIGHT	NY Metro	982	930	993	0	0
+Bad cards must make.	OVERNIGHT	North Midwest	907	990	955	0	0
+Bad cards must make.	REGULAR	Mid Atlantic	928	974	1005	0	0
+Bad cards must make.	REGULAR	NY Metro	942	1009	948	0	0
+Bad cards must make.	REGULAR	North Midwest	921	968	925	0	0
+Bad cards must make.	TWO DAY	Mid Atlantic	954	971	979	0	0
+Bad cards must make.	TWO DAY	NY Metro	947	1013	952	0	0
+Bad cards must make.	TWO DAY	North Midwest	904	930	947	0	0
+Conventional childr	EXPRESS	Mid Atlantic	1214	1296	1243	0	0
+Conventional childr	EXPRESS	NY Metro	1284	1331	1277	0	0
+Conventional childr	EXPRESS	North Midwest	1180	1287	1226	0	0
+Conventional childr	LIBRARY	Mid Atlantic	990	1008	962	0	0
+Conventional childr	LIBRARY	NY Metro	983	976	981	0	0
+Conventional childr	LIBRARY	North Midwest	925	892	953	0	0
+Conventional childr	NEXT DAY	Mid Atlantic	1284	1315	1274	0	0
+Conventional childr	NEXT DAY	NY Metro	1288	1280	1284	0	0
+Conventional childr	NEXT DAY	North Midwest	1249	1278	1308	0	0
+Conventional childr	OVERNIGHT	Mid Atlantic	897	973	926	0	0
+Conventional childr	OVERNIGHT	NY Metro	958	963	897	0	0
+Conventional childr	OVERNIGHT	North Midwest	942	934	937	0	0
+Conventional childr	REGULAR	Mid Atlantic	941	1007	945	0	0
+Conventional childr	REGULAR	NY Metro	949	928	987	0	0
+Conventional childr	REGULAR	North Midwest	920	927	925	0	0
+Conventional childr	TWO DAY	Mid Atlantic	926	979	1047	0	0
+Conventional childr	TWO DAY	NY Metro	925	1007	962	0	0
+Conventional childr	TWO DAY	North Midwest	879	980	971	0	0
+Doors canno	EXPRESS	Mid Atlantic	1240	1245	1267	0	0
+Doors canno	EXPRESS	NY Metro	1234	1270	1349	0	0
+Doors canno	EXPRESS	North Midwest	1185	1238	1197	0	0
+Doors canno	LIBRARY	Mid Atlantic	873	974	948	0	0
+Doors canno	LIBRARY	NY Metro	987	1005	990	0	0
+Doors canno	LIBRARY	North Midwest	947	952	1025	0	0
+Doors canno	NEXT DAY	Mid Atlantic	1264	1276	1185	0	0
+Doors canno	NEXT DAY	NY Metro	1263	1337	1310	0	0
+Doors canno	NEXT DAY	North Midwest	1202	1247	1278	0	0
+Doors canno	OVERNIGHT	Mid Atlantic	912	888	934	0	0
+Doors canno	OVERNIGHT	NY Metro	942	1003	959	0	0
+Doors canno	OVERNIGHT	North Midwest	896	903	911	0	0
+Doors canno	REGULAR	Mid Atlantic	888	953	974	0	0
+Doors canno	REGULAR	NY Metro	971	1028	946	0	0
+Doors canno	REGULAR	North Midwest	937	974	914	0	0
+Doors canno	TWO DAY	Mid Atlantic	974	898	985	0	0
+Doors canno	TWO DAY	NY Metro	982	1017	997	0	0
+Doors canno	TWO DAY	North Midwest	985	937	959	0	0
+Important issues liv	EXPRESS	Mid Atlantic	1322	1329	1293	0	0
+Important issues liv	EXPRESS	NY Metro	1301	1300	1239	0	0
+Important issues liv	EXPRESS	North Midwest	1266	1228	1285	0	0
+Important issues liv	LIBRARY	Mid Atlantic	988	997	925	0	0
+Important issues liv	LIBRARY	NY Metro	947	963	988	0	0
+Important issues liv	LIBRARY	North Midwest	937	972	964	0	0
+Important issues liv	NEXT DAY	Mid Atlantic	1221	1268	1217	0	0
+Important issues liv	NEXT DAY	NY Metro	1281	1311	1310	0	0
+Important issues liv	NEXT DAY	North Midwest	1219	1214	1327	0	0
+Important issues liv	OVERNIGHT	Mid Atlantic	929	1018	991	0	0
+Important issues liv	OVERNIGHT	NY Metro	950	965	931	0	0
+Important issues liv	OVERNIGHT	North Midwest	936	989	932	0	0
+Important issues liv	REGULAR	Mid Atlantic	961	995	949	0	0
+Important issues liv	REGULAR	NY Metro	972	934	1018	0	0
+Important issues liv	REGULAR	North Midwest	905	947	941	0	0
+Important issues liv	TWO DAY	Mid Atlantic	954	982	944	0	0
+Important issues liv	TWO DAY	NY Metro	844	972	1006	0	0
+Important issues liv	TWO DAY	North Midwest	914	969	960	0	0
+
diff --git a/regression-test/suites/datev2/ssb_sf1_p1/load.groovy b/regression-test/suites/datev2/ssb_sf1_p1/load.groovy
index 6a9992177e..fe0cead6f4 100644
--- a/regression-test/suites/datev2/ssb_sf1_p1/load.groovy
+++ b/regression-test/suites/datev2/ssb_sf1_p1/load.groovy
@@ -60,7 +60,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/load.groovy b/regression-test/suites/datev2/tpcds_sf1_p1/load.groovy
index 7215cbed6e..78927b93f1 100644
--- a/regression-test/suites/datev2/tpcds_sf1_p1/load.groovy
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/load.groovy
@@ -78,7 +78,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpcds/sf1/${tableName}.dat.gz"""
+            file """${getS3Url()}/regression/tpcds/sf1/${tableName}.dat.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q01.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q01.sql
new file mode 100644
index 0000000000..e1c738b301
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q01.sql
@@ -0,0 +1,29 @@
+WITH
+  customer_total_return AS (
+   SELECT
+     sr_customer_sk ctr_customer_sk
+   , sr_store_sk ctr_store_sk
+   , sum(sr_return_amt) ctr_total_return
+   FROM
+     store_returns
+   , date_dim
+   WHERE (sr_returned_date_sk = d_date_sk)
+      AND (d_year = 2000)
+   GROUP BY sr_customer_sk, sr_store_sk
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ c_customer_id
+FROM
+  customer_total_return ctr1
+, store
+, customer
+WHERE (ctr1.ctr_total_return > (
+      SELECT (avg(ctr_total_return) * 1.2)
+      FROM
+        customer_total_return ctr2
+      WHERE (ctr1.ctr_store_sk = ctr2.ctr_store_sk)
+   ))
+   AND (s_store_sk = ctr1.ctr_store_sk)
+   AND (s_state = 'TN')
+   AND (ctr1.ctr_customer_sk = c_customer_sk)
+ORDER BY c_customer_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q02.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q02.sql
new file mode 100644
index 0000000000..125d393749
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q02.sql
@@ -0,0 +1,80 @@
+WITH
+  wscs AS (
+   SELECT
+     sold_date_sk
+   , sales_price
+   FROM
+     (
+      SELECT
+        ws_sold_date_sk sold_date_sk
+      , ws_ext_sales_price sales_price
+      FROM
+        web_sales
+   ) x
+UNION ALL (
+      SELECT
+        cs_sold_date_sk sold_date_sk
+      , cs_ext_sales_price sales_price
+      FROM
+        catalog_sales
+   ) )
+, wswscs AS (
+   SELECT
+     d_week_seq
+   , sum((CASE WHEN (d_day_name = 'Sunday') THEN sales_price ELSE null END)) sun_sales
+   , sum((CASE WHEN (d_day_name = 'Monday') THEN sales_price ELSE null END)) mon_sales
+   , sum((CASE WHEN (d_day_name = 'Tuesday') THEN sales_price ELSE null END)) tue_sales
+   , sum((CASE WHEN (d_day_name = 'Wednesday') THEN sales_price ELSE null END)) wed_sales
+   , sum((CASE WHEN (d_day_name = 'Thursday') THEN sales_price ELSE null END)) thu_sales
+   , sum((CASE WHEN (d_day_name = 'Friday') THEN sales_price ELSE null END)) fri_sales
+   , sum((CASE WHEN (d_day_name = 'Saturday') THEN sales_price ELSE null END)) sat_sales
+   FROM
+     wscs
+   , date_dim
+   WHERE (d_date_sk = sold_date_sk)
+   GROUP BY d_week_seq
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  d_week_seq1
+, round((sun_sales1 / sun_sales2), 2)
+, round((mon_sales1 / mon_sales2), 2)
+, round((tue_sales1 / tue_sales2), 2)
+, round((wed_sales1 / wed_sales2), 2)
+, round((thu_sales1 / thu_sales2), 2)
+, round((fri_sales1 / fri_sales2), 2)
+, round((sat_sales1 / sat_sales2), 2)
+FROM
+  (
+   SELECT
+     wswscs.d_week_seq d_week_seq1
+   , sun_sales sun_sales1
+   , mon_sales mon_sales1
+   , tue_sales tue_sales1
+   , wed_sales wed_sales1
+   , thu_sales thu_sales1
+   , fri_sales fri_sales1
+   , sat_sales sat_sales1
+   FROM
+     wswscs
+   , date_dim
+   WHERE (date_dim.d_week_seq = wswscs.d_week_seq)
+      AND (d_year = 2001)
+)  y
+, (
+   SELECT
+     wswscs.d_week_seq d_week_seq2
+   , sun_sales sun_sales2
+   , mon_sales mon_sales2
+   , tue_sales tue_sales2
+   , wed_sales wed_sales2
+   , thu_sales thu_sales2
+   , fri_sales fri_sales2
+   , sat_sales sat_sales2
+   FROM
+     wswscs
+   , date_dim
+   WHERE (date_dim.d_week_seq = wswscs.d_week_seq)
+      AND (d_year = (2001 + 1))
+)  z
+WHERE (d_week_seq1 = (d_week_seq2 - 53))
+ORDER BY d_week_seq1 ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q03.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q03.sql
new file mode 100644
index 0000000000..ac94f81cc5
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q03.sql
@@ -0,0 +1,16 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  dt.d_year
+, item.i_brand_id brand_id
+, item.i_brand brand
+, sum(ss_ext_sales_price) sum_agg
+FROM
+  date_dim dt
+, store_sales
+, item
+WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
+   AND (store_sales.ss_item_sk = item.i_item_sk)
+   AND (item.i_manufact_id = 128)
+   AND (dt.d_moy = 11)
+GROUP BY dt.d_year, item.i_brand, item.i_brand_id
+ORDER BY dt.d_year ASC, sum_agg DESC, brand_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q04.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q04.sql
new file mode 100644
index 0000000000..e5203461c5
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q04.sql
@@ -0,0 +1,93 @@
+WITH
+  year_total AS (
+   SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , c_preferred_cust_flag customer_preferred_cust_flag
+   , c_birth_country customer_birth_country
+   , c_login customer_login
+   , c_email_address customer_email_address
+   , d_year dyear
+   , sum(((((ss_ext_list_price - ss_ext_wholesale_cost) - ss_ext_discount_amt) + ss_ext_sales_price) / 2)) year_total
+   , 's' sale_type
+   FROM
+     customer
+   , store_sales
+   , date_dim
+   WHERE (c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+   GROUP BY c_customer_id, c_first_name, c_last_name, c_preferred_cust_flag, c_birth_country, c_login, c_email_address, d_year
+UNION ALL    SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , c_preferred_cust_flag customer_preferred_cust_flag
+   , c_birth_country customer_birth_country
+   , c_login customer_login
+   , c_email_address customer_email_address
+   , d_year dyear
+   , sum(((((cs_ext_list_price - cs_ext_wholesale_cost) - cs_ext_discount_amt) + cs_ext_sales_price) / 2)) year_total
+   , 'c' sale_type
+   FROM
+     customer
+   , catalog_sales
+   , date_dim
+   WHERE (c_customer_sk = cs_bill_customer_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+   GROUP BY c_customer_id, c_first_name, c_last_name, c_preferred_cust_flag, c_birth_country, c_login, c_email_address, d_year
+UNION ALL    SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , c_preferred_cust_flag customer_preferred_cust_flag
+   , c_birth_country customer_birth_country
+   , c_login customer_login
+   , c_email_address customer_email_address
+   , d_year dyear
+   , sum(((((ws_ext_list_price - ws_ext_wholesale_cost) - ws_ext_discount_amt) + ws_ext_sales_price) / 2)) year_total
+   , 'w' sale_type
+   FROM
+     customer
+   , web_sales
+   , date_dim
+   WHERE (c_customer_sk = ws_bill_customer_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+   GROUP BY c_customer_id, c_first_name, c_last_name, c_preferred_cust_flag, c_birth_country, c_login, c_email_address, d_year
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  t_s_secyear.customer_id
+, t_s_secyear.customer_first_name
+, t_s_secyear.customer_last_name
+, t_s_secyear.customer_preferred_cust_flag
+FROM
+  year_total t_s_firstyear
+, year_total t_s_secyear
+, year_total t_c_firstyear
+, year_total t_c_secyear
+, year_total t_w_firstyear
+, year_total t_w_secyear
+WHERE (t_s_secyear.customer_id = t_s_firstyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_c_secyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_c_firstyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_firstyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_secyear.customer_id)
+   AND (t_s_firstyear.sale_type = 's')
+   AND (t_c_firstyear.sale_type = 'c')
+   AND (t_w_firstyear.sale_type = 'w')
+   AND (t_s_secyear.sale_type = 's')
+   AND (t_c_secyear.sale_type = 'c')
+   AND (t_w_secyear.sale_type = 'w')
+   AND (t_s_firstyear.dyear = 2001)
+   AND (t_s_secyear.dyear = (2001 + 1))
+   AND (t_c_firstyear.dyear = 2001)
+   AND (t_c_secyear.dyear = (2001 + 1))
+   AND (t_w_firstyear.dyear = 2001)
+   AND (t_w_secyear.dyear = (2001 + 1))
+   AND (t_s_firstyear.year_total > 0)
+   AND (t_c_firstyear.year_total > 0)
+   AND (t_w_firstyear.year_total > 0)
+   AND ((CASE WHEN (t_c_firstyear.year_total > 0) THEN (t_c_secyear.year_total / t_c_firstyear.year_total) ELSE null END) > (CASE WHEN (t_s_firstyear.year_total > 0) THEN (t_s_secyear.year_total / t_s_firstyear.year_total) ELSE null END))
+   AND ((CASE WHEN (t_c_firstyear.year_total > 0) THEN (t_c_secyear.year_total / t_c_firstyear.year_total) ELSE null END) > (CASE WHEN (t_w_firstyear.year_total > 0) THEN (t_w_secyear.year_total / t_w_firstyear.year_total) ELSE null END))
+ORDER BY t_s_secyear.customer_id ASC, t_s_secyear.customer_first_name ASC, t_s_secyear.customer_last_name ASC, t_s_secyear.customer_preferred_cust_flag ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q05.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q05.sql
new file mode 100644
index 0000000000..e65ad57344
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q05.sql
@@ -0,0 +1,144 @@
+WITH
+  ssr AS (
+   SELECT
+     s_store_id
+   , sum(sales_price) sales
+   , sum(profit) profit
+   , sum(return_amt) returns
+   , sum(net_loss) profit_loss
+   FROM
+     (
+      SELECT
+        ss_store_sk store_sk
+      , ss_sold_date_sk date_sk
+      , ss_ext_sales_price sales_price
+      , ss_net_profit profit
+      , CAST(0 AS DECIMALV3(7,2)) return_amt
+      , CAST(0 AS DECIMALV3(7,2)) net_loss
+      FROM
+        store_sales
+UNION ALL       SELECT
+        sr_store_sk store_sk
+      , sr_returned_date_sk date_sk
+      , CAST(0 AS DECIMALV3(7,2)) sales_price
+      , CAST(0 AS DECIMALV3(7,2)) profit
+      , sr_return_amt return_amt
+      , sr_net_loss net_loss
+      FROM
+        store_returns
+   )  salesreturns
+   , date_dim
+   , store
+   WHERE (date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '14' DAY))
+      AND (store_sk = s_store_sk)
+   GROUP BY s_store_id
+)
+, csr AS (
+   SELECT
+     cp_catalog_page_id
+   , sum(sales_price) sales
+   , sum(profit) profit
+   , sum(return_amt) returns
+   , sum(net_loss) profit_loss
+   FROM
+     (
+      SELECT
+        cs_catalog_page_sk page_sk
+      , cs_sold_date_sk date_sk
+      , cs_ext_sales_price sales_price
+      , cs_net_profit profit
+      , CAST(0 AS DECIMALV3(7,2)) return_amt
+      , CAST(0 AS DECIMALV3(7,2)) net_loss
+      FROM
+        catalog_sales
+UNION ALL       SELECT
+        cr_catalog_page_sk page_sk
+      , cr_returned_date_sk date_sk
+      , CAST(0 AS DECIMALV3(7,2)) sales_price
+      , CAST(0 AS DECIMALV3(7,2)) profit
+      , cr_return_amount return_amt
+      , cr_net_loss net_loss
+      FROM
+        catalog_returns
+   )  salesreturns
+   , date_dim
+   , catalog_page
+   WHERE (date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '14' DAY))
+      AND (page_sk = cp_catalog_page_sk)
+   GROUP BY cp_catalog_page_id
+)
+, wsr AS (
+   SELECT
+     web_site_id
+   , sum(sales_price) sales
+   , sum(profit) profit
+   , sum(return_amt) returns
+   , sum(net_loss) profit_loss
+   FROM
+     (
+      SELECT
+        ws_web_site_sk wsr_web_site_sk
+      , ws_sold_date_sk date_sk
+      , ws_ext_sales_price sales_price
+      , ws_net_profit profit
+      , CAST(0 AS DECIMALV3(7,2)) return_amt
+      , CAST(0 AS DECIMALV3(7,2)) net_loss
+      FROM
+        web_sales
+UNION ALL       SELECT
+        ws_web_site_sk wsr_web_site_sk
+      , wr_returned_date_sk date_sk
+      , CAST(0 AS DECIMALV3(7,2)) sales_price
+      , CAST(0 AS DECIMALV3(7,2)) profit
+      , wr_return_amt return_amt
+      , wr_net_loss net_loss
+      FROM
+        web_returns
+      LEFT JOIN web_sales ON (wr_item_sk = ws_item_sk)
+         AND (wr_order_number = ws_order_number)
+   )  salesreturns
+   , date_dim
+   , web_site
+   WHERE (date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '14' DAY))
+      AND (wsr_web_site_sk = web_site_sk)
+   GROUP BY web_site_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  channel
+, id
+, sum(sales) sales
+, sum(returns) returns
+, sum(profit) profit
+FROM
+  (
+   SELECT
+     'store channel' channel
+   , concat('store', s_store_id) id
+   , sales
+   , returns
+   , (profit - profit_loss) profit
+   FROM
+     ssr
+UNION ALL    SELECT
+     'catalog channel' channel
+   , concat('catalog_page', cp_catalog_page_id) id
+   , sales
+   , returns
+   , (profit - profit_loss) profit
+   FROM
+     csr
+UNION ALL    SELECT
+     'web channel' channel
+   , concat('web_site', web_site_id) id
+   , sales
+   , returns
+   , (profit - profit_loss) profit
+   FROM
+     wsr
+)  x
+GROUP BY ROLLUP (channel, id)
+ORDER BY channel ASC, id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q06.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q06.sql
new file mode 100644
index 0000000000..421f43cf49
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q06.sql
@@ -0,0 +1,31 @@
+--- takes over 30 minutes on travis to complete
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  a.ca_state STATE
+, count(*) cnt
+FROM
+  customer_address a
+, customer c
+, store_sales s
+, date_dim d
+, item i
+WHERE (a.ca_address_sk = c.c_current_addr_sk)
+   AND (c.c_customer_sk = s.ss_customer_sk)
+   AND (s.ss_sold_date_sk = d.d_date_sk)
+   AND (s.ss_item_sk = i.i_item_sk)
+   AND (d.d_month_seq = (
+      SELECT DISTINCT d_month_seq
+      FROM
+        date_dim
+      WHERE (d_year = 2001)
+         AND (d_moy = 1)
+   ))
+   AND (i.i_current_price > (1.2 * (
+         SELECT avg(j.i_current_price)
+         FROM
+           item j
+         WHERE (j.i_category = i.i_category)
+      )))
+GROUP BY a.ca_state
+HAVING (count(*) >= 10)
+ORDER BY cnt ASC, a.ca_state ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q07.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q07.sql
new file mode 100644
index 0000000000..8dff6e2062
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q07.sql
@@ -0,0 +1,25 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, avg(ss_quantity) agg1
+, avg(ss_list_price) agg2
+, avg(ss_coupon_amt) agg3
+, avg(ss_sales_price) agg4
+FROM
+  store_sales
+, customer_demographics
+, date_dim
+, item
+, promotion
+WHERE (ss_sold_date_sk = d_date_sk)
+   AND (ss_item_sk = i_item_sk)
+   AND (ss_cdemo_sk = cd_demo_sk)
+   AND (ss_promo_sk = p_promo_sk)
+   AND (cd_gender = 'M')
+   AND (cd_marital_status = 'S')
+   AND (cd_education_status = 'College')
+   AND ((p_channel_email = 'N')
+      OR (p_channel_event = 'N'))
+   AND (d_year = 2000)
+GROUP BY i_item_id
+ORDER BY i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q08.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q08.sql
new file mode 100644
index 0000000000..6f86d66cc5
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q08.sql
@@ -0,0 +1,441 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  s_store_name
+, sum(ss_net_profit)
+FROM
+  store_sales
+, date_dim
+, store
+, (
+   SELECT ca_zip
+   FROM
+     (
+(
+         SELECT substr(ca_zip, 1, 5) ca_zip
+         FROM
+           customer_address
+         WHERE (substr(ca_zip, 1, 5) IN (
+                '24128'
+              , '57834'
+              , '13354'
+              , '15734'
+              , '78668'
+              , '76232'
+              , '62878'
+              , '45375'
+              , '63435'
+              , '22245'
+              , '65084'
+              , '49130'
+              , '40558'
+              , '25733'
+              , '15798'
+              , '87816'
+              , '81096'
+              , '56458'
+              , '35474'
+              , '27156'
+              , '83926'
+              , '18840'
+              , '28286'
+              , '24676'
+              , '37930'
+              , '77556'
+              , '27700'
+              , '45266'
+              , '94627'
+              , '62971'
+              , '20548'
+              , '23470'
+              , '47305'
+              , '53535'
+              , '21337'
+              , '26231'
+              , '50412'
+              , '69399'
+              , '17879'
+              , '51622'
+              , '43848'
+              , '21195'
+              , '83921'
+              , '15559'
+              , '67853'
+              , '15126'
+              , '16021'
+              , '26233'
+              , '53268'
+              , '10567'
+              , '91137'
+              , '76107'
+              , '11101'
+              , '59166'
+              , '38415'
+              , '61265'
+              , '71954'
+              , '15371'
+              , '11928'
+              , '15455'
+              , '98294'
+              , '68309'
+              , '69913'
+              , '59402'
+              , '58263'
+              , '25782'
+              , '18119'
+              , '35942'
+              , '33282'
+              , '42029'
+              , '17920'
+              , '98359'
+              , '15882'
+              , '45721'
+              , '60279'
+              , '18426'
+              , '64544'
+              , '25631'
+              , '43933'
+              , '37125'
+              , '98235'
+              , '10336'
+              , '24610'
+              , '68101'
+              , '56240'
+              , '40081'
+              , '86379'
+              , '44165'
+              , '33515'
+              , '88190'
+              , '84093'
+              , '27068'
+              , '99076'
+              , '36634'
+              , '50308'
+              , '28577'
+              , '39736'
+              , '33786'
+              , '71286'
+              , '26859'
+              , '55565'
+              , '98569'
+              , '70738'
+              , '19736'
+              , '64457'
+              , '17183'
+              , '28915'
+              , '26653'
+              , '58058'
+              , '89091'
+              , '54601'
+              , '24206'
+              , '14328'
+              , '55253'
+              , '82136'
+              , '67897'
+              , '56529'
+              , '72305'
+              , '67473'
+              , '62377'
+              , '22752'
+              , '57647'
+              , '62496'
+              , '41918'
+              , '36233'
+              , '86284'
+              , '54917'
+              , '22152'
+              , '19515'
+              , '63837'
+              , '18376'
+              , '42961'
+              , '10144'
+              , '36495'
+              , '58078'
+              , '38607'
+              , '91110'
+              , '64147'
+              , '19430'
+              , '17043'
+              , '45200'
+              , '63981'
+              , '48425'
+              , '22351'
+              , '30010'
+              , '21756'
+              , '14922'
+              , '14663'
+              , '77191'
+              , '60099'
+              , '29741'
+              , '36420'
+              , '21076'
+              , '91393'
+              , '28810'
+              , '96765'
+              , '23006'
+              , '18799'
+              , '49156'
+              , '98025'
+              , '23932'
+              , '67467'
+              , '30450'
+              , '50298'
+              , '29178'
+              , '89360'
+              , '32754'
+              , '63089'
+              , '87501'
+              , '87343'
+              , '29839'
+              , '30903'
+              , '81019'
+              , '18652'
+              , '73273'
+              , '25989'
+              , '20260'
+              , '68893'
+              , '53179'
+              , '30469'
+              , '28898'
+              , '31671'
+              , '24996'
+              , '18767'
+              , '64034'
+              , '91068'
+              , '51798'
+              , '51200'
+              , '63193'
+              , '39516'
+              , '72550'
+              , '72325'
+              , '51211'
+              , '23968'
+              , '86057'
+              , '10390'
+              , '85816'
+              , '45692'
+              , '65164'
+              , '21309'
+              , '18845'
+              , '68621'
+              , '92712'
+              , '68880'
+              , '90257'
+              , '47770'
+              , '13955'
+              , '70466'
+              , '21286'
+              , '67875'
+              , '82636'
+              , '36446'
+              , '79994'
+              , '72823'
+              , '40162'
+              , '41367'
+              , '41766'
+              , '22437'
+              , '58470'
+              , '11356'
+              , '76638'
+              , '68806'
+              , '25280'
+              , '67301'
+              , '73650'
+              , '86198'
+              , '16725'
+              , '38935'
+              , '13394'
+              , '61810'
+              , '81312'
+              , '15146'
+              , '71791'
+              , '31016'
+              , '72013'
+              , '37126'
+              , '22744'
+              , '73134'
+              , '70372'
+              , '30431'
+              , '39192'
+              , '35850'
+              , '56571'
+              , '67030'
+              , '22461'
+              , '88424'
+              , '88086'
+              , '14060'
+              , '40604'
+              , '19512'
+              , '72175'
+              , '51649'
+              , '19505'
+              , '24317'
+              , '13375'
+              , '81426'
+              , '18270'
+              , '72425'
+              , '45748'
+              , '55307'
+              , '53672'
+              , '52867'
+              , '56575'
+              , '39127'
+              , '30625'
+              , '10445'
+              , '39972'
+              , '74351'
+              , '26065'
+              , '83849'
+              , '42666'
+              , '96976'
+              , '68786'
+              , '77721'
+              , '68908'
+              , '66864'
+              , '63792'
+              , '51650'
+              , '31029'
+              , '26689'
+              , '66708'
+              , '11376'
+              , '20004'
+              , '31880'
+              , '96451'
+              , '41248'
+              , '94898'
+              , '18383'
+              , '60576'
+              , '38193'
+              , '48583'
+              , '13595'
+              , '76614'
+              , '24671'
+              , '46820'
+              , '82276'
+              , '10516'
+              , '11634'
+              , '45549'
+              , '88885'
+              , '18842'
+              , '90225'
+              , '18906'
+              , '13376'
+              , '84935'
+              , '78890'
+              , '58943'
+              , '15765'
+              , '50016'
+              , '69035'
+              , '49448'
+              , '39371'
+              , '41368'
+              , '33123'
+              , '83144'
+              , '14089'
+              , '94945'
+              , '73241'
+              , '19769'
+              , '47537'
+              , '38122'
+              , '28587'
+              , '76698'
+              , '22927'
+              , '56616'
+              , '34425'
+              , '96576'
+              , '78567'
+              , '97789'
+              , '94983'
+              , '79077'
+              , '57855'
+              , '97189'
+              , '46081'
+              , '48033'
+              , '19849'
+              , '28488'
+              , '28545'
+              , '72151'
+              , '69952'
+              , '43285'
+              , '26105'
+              , '76231'
+              , '15723'
+              , '25486'
+              , '39861'
+              , '83933'
+              , '75691'
+              , '46136'
+              , '61547'
+              , '66162'
+              , '25858'
+              , '22246'
+              , '51949'
+              , '27385'
+              , '77610'
+              , '34322'
+              , '51061'
+              , '68100'
+              , '61860'
+              , '13695'
+              , '44438'
+              , '90578'
+              , '96888'
+              , '58048'
+              , '99543'
+              , '73171'
+              , '56691'
+              , '64528'
+              , '56910'
+              , '83444'
+              , '30122'
+              , '68014'
+              , '14171'
+              , '16807'
+              , '83041'
+              , '34102'
+              , '51103'
+              , '79777'
+              , '17871'
+              , '12305'
+              , '22685'
+              , '94167'
+              , '28709'
+              , '35258'
+              , '57665'
+              , '71256'
+              , '57047'
+              , '11489'
+              , '31387'
+              , '68341'
+              , '78451'
+              , '14867'
+              , '25103'
+              , '35458'
+              , '25003'
+              , '54364'
+              , '73520'
+              , '32213'
+              , '35576'))
+      )       INTERSECT (
+         SELECT ca_zip
+         FROM
+           (
+            SELECT
+              substr(ca_zip, 1, 5) ca_zip
+            , count(*) cnt
+            FROM
+              customer_address
+            , customer
+            WHERE (ca_address_sk = c_current_addr_sk)
+               AND (c_preferred_cust_flag = 'Y')
+            GROUP BY ca_zip
+            HAVING (count(*) > 10)
+         )  a1
+      )    )  a2
+)  v1
+WHERE (ss_store_sk = s_store_sk)
+   AND (ss_sold_date_sk = d_date_sk)
+   AND (d_qoy = 2)
+   AND (d_year = 1998)
+   AND (substr(s_zip, 1, 2) = substr(v1.ca_zip, 1, 2))
+GROUP BY s_store_name
+ORDER BY s_store_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q09.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q09.sql
new file mode 100644
index 0000000000..83ba7c807e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q09.sql
@@ -0,0 +1,84 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  (CASE WHEN ((
+      SELECT count(*)
+      FROM
+        store_sales
+      WHERE (ss_quantity BETWEEN 1 AND 20)
+   ) > 74129) THEN (
+   SELECT avg(ss_ext_discount_amt)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 1 AND 20)
+) ELSE (
+   SELECT avg(ss_net_paid)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 1 AND 20)
+) END) bucket1
+, (CASE WHEN ((
+      SELECT count(*)
+      FROM
+        store_sales
+      WHERE (ss_quantity BETWEEN 21 AND 40)
+   ) > 122840) THEN (
+   SELECT avg(ss_ext_discount_amt)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 21 AND 40)
+) ELSE (
+   SELECT avg(ss_net_paid)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 21 AND 40)
+) END) bucket2
+, (CASE WHEN ((
+      SELECT count(*)
+      FROM
+        store_sales
+      WHERE (ss_quantity BETWEEN 41 AND 60)
+   ) > 56580) THEN (
+   SELECT avg(ss_ext_discount_amt)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 41 AND 60)
+) ELSE (
+   SELECT avg(ss_net_paid)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 41 AND 60)
+) END) bucket3
+, (CASE WHEN ((
+      SELECT count(*)
+      FROM
+        store_sales
+      WHERE (ss_quantity BETWEEN 61 AND 80)
+   ) > 10097) THEN (
+   SELECT avg(ss_ext_discount_amt)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 61 AND 80)
+) ELSE (
+   SELECT avg(ss_net_paid)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 61 AND 80)
+) END) bucket4
+, (CASE WHEN ((
+      SELECT count(*)
+      FROM
+        store_sales
+      WHERE (ss_quantity BETWEEN 81 AND 100)
+   ) > 165306) THEN (
+   SELECT avg(ss_ext_discount_amt)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 81 AND 100)
+) ELSE (
+   SELECT avg(ss_net_paid)
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 81 AND 100)
+) END) bucket5
+FROM
+  reason
+WHERE (r_reason_sk = 1)
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q10.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q10.sql
new file mode 100644
index 0000000000..f858219be1
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q10.sql
@@ -0,0 +1,57 @@
+/*
+SELECT
+  cd_gender
+, cd_marital_status
+, cd_education_status
+, count(*) cnt1
+, cd_purchase_estimate
+, count(*) cnt2
+, cd_credit_rating
+, count(*) cnt3
+, cd_dep_count
+, count(*) cnt4
+, cd_dep_employed_count
+, count(*) cnt5
+, cd_dep_college_count
+, count(*) cnt6
+FROM
+  customer c
+, customer_address ca
+, customer_demographics
+WHERE (c.c_current_addr_sk = ca.ca_address_sk)
+   AND (ca_county IN ('Rush County', 'Toole County', 'Jefferson County', 'Dona Ana County', 'La Porte County'))
+   AND (cd_demo_sk = c.c_current_cdemo_sk)
+   AND (EXISTS (
+   SELECT *
+   FROM
+     store_sales
+   , date_dim
+   WHERE (c.c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 2002)
+      AND (d_moy BETWEEN 1 AND (1 + 3))
+))
+   AND ((EXISTS (
+      SELECT *
+      FROM
+        web_sales
+      , date_dim
+      WHERE (c.c_customer_sk = ws_bill_customer_sk)
+         AND (ws_sold_date_sk = d_date_sk)
+         AND (d_year = 2002)
+         AND (d_moy BETWEEN 1 AND (1 + 3))
+   ))
+      OR (EXISTS (
+      SELECT *
+      FROM
+        catalog_sales
+      , date_dim
+      WHERE (c.c_customer_sk = cs_ship_customer_sk)
+         AND (cs_sold_date_sk = d_date_sk)
+         AND (d_year = 2002)
+         AND (d_moy BETWEEN 1 AND (1 + 3))
+   )))
+GROUP BY cd_gender, cd_marital_status, cd_education_status, cd_purchase_estimate, cd_credit_rating, cd_dep_count, cd_dep_employed_count, cd_dep_college_count
+ORDER BY cd_gender ASC, cd_marital_status ASC, cd_education_status ASC, cd_purchase_estimate ASC, cd_credit_rating ASC, cd_dep_count ASC, cd_dep_employed_count ASC, cd_dep_college_count ASC
+LIMIT 100
+*/
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q11.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q11.sql
new file mode 100644
index 0000000000..9dd8e3df98
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q11.sql
@@ -0,0 +1,67 @@
+WITH
+  year_total AS (
+   SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , c_preferred_cust_flag customer_preferred_cust_flag
+   , c_birth_country customer_birth_country
+   , c_login customer_login
+   , c_email_address customer_email_address
+   , d_year dyear
+   , sum((ss_ext_list_price - ss_ext_discount_amt)) year_total
+   , 's' sale_type
+   FROM
+     customer
+   , store_sales
+   , date_dim
+   WHERE (c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+   GROUP BY c_customer_id, c_first_name, c_last_name, c_preferred_cust_flag, c_birth_country, c_login, c_email_address, d_year
+UNION ALL    SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , c_preferred_cust_flag customer_preferred_cust_flag
+   , c_birth_country customer_birth_country
+   , c_login customer_login
+   , c_email_address customer_email_address
+   , d_year dyear
+   , sum((ws_ext_list_price - ws_ext_discount_amt)) year_total
+   , 'w' sale_type
+   FROM
+     customer
+   , web_sales
+   , date_dim
+   WHERE (c_customer_sk = ws_bill_customer_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+   GROUP BY c_customer_id, c_first_name, c_last_name, c_preferred_cust_flag, c_birth_country, c_login, c_email_address, d_year
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  t_s_secyear.customer_id
+, t_s_secyear.customer_first_name
+, t_s_secyear.customer_last_name
+, t_s_secyear.customer_preferred_cust_flag
+, t_s_secyear.customer_birth_country
+, t_s_secyear.customer_login
+FROM
+  year_total t_s_firstyear
+, year_total t_s_secyear
+, year_total t_w_firstyear
+, year_total t_w_secyear
+WHERE (t_s_secyear.customer_id = t_s_firstyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_secyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_firstyear.customer_id)
+   AND (t_s_firstyear.sale_type = 's')
+   AND (t_w_firstyear.sale_type = 'w')
+   AND (t_s_secyear.sale_type = 's')
+   AND (t_w_secyear.sale_type = 'w')
+   AND (t_s_firstyear.dyear = 2001)
+   AND (t_s_secyear.dyear = (2001 + 1))
+   AND (t_w_firstyear.dyear = 2001)
+   AND (t_w_secyear.dyear = (2001 + 1))
+   AND (t_s_firstyear.year_total > 0)
+   AND (t_w_firstyear.year_total > 0)
+   AND ((CASE WHEN (t_w_firstyear.year_total > 0) THEN (t_w_secyear.year_total / t_w_firstyear.year_total) ELSE 0.0 END) > (CASE WHEN (t_s_firstyear.year_total > 0) THEN (t_s_secyear.year_total / t_s_firstyear.year_total) ELSE 0.0 END))
+ORDER BY t_s_secyear.customer_id ASC, t_s_secyear.customer_first_name ASC, t_s_secyear.customer_last_name ASC, t_s_secyear.customer_preferred_cust_flag ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q12.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q12.sql
new file mode 100644
index 0000000000..c3ff2b268a
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q12.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, i_category
+, i_class
+, i_current_price
+, sum(ws_ext_sales_price) itemrevenue
+, ((sum(ws_ext_sales_price) * 100) / sum(sum(ws_ext_sales_price)) OVER (PARTITION BY i_class)) revenueratio
+FROM
+  web_sales
+, item
+, date_dim
+WHERE (ws_item_sk = i_item_sk)
+   AND (i_category IN ('Sports', 'Books', 'Home'))
+   AND (ws_sold_date_sk = d_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN CAST('1999-02-22' AS DATE) AND (CAST('1999-02-22' AS DATE) + INTERVAL  '30' DAY))
+GROUP BY i_item_id, i_item_desc, i_category, i_class, i_current_price
+ORDER BY i_category ASC, i_class ASC, i_item_id ASC, i_item_desc ASC, revenueratio ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q13.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q13.sql
new file mode 100644
index 0000000000..fccd355adb
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q13.sql
@@ -0,0 +1,45 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  avg(ss_quantity)
+, avg(ss_ext_sales_price)
+, avg(ss_ext_wholesale_cost)
+, sum(ss_ext_wholesale_cost)
+FROM
+  store_sales
+, store
+, customer_demographics
+, household_demographics
+, customer_address
+, date_dim
+WHERE (s_store_sk = ss_store_sk)
+   AND (ss_sold_date_sk = d_date_sk)
+   AND (d_year = 2001)
+   AND (((ss_hdemo_sk = hd_demo_sk)
+         AND (cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'M')
+         AND (cd_education_status = 'Advanced Degree')
+         AND (ss_sales_price BETWEEN 100.00 AND 150.00)
+         AND (hd_dep_count = 3))
+      OR ((ss_hdemo_sk = hd_demo_sk)
+         AND (cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'S')
+         AND (cd_education_status = 'College')
+         AND (ss_sales_price BETWEEN 50.00 AND 100.00)
+         AND (hd_dep_count = 1))
+      OR ((ss_hdemo_sk = hd_demo_sk)
+         AND (cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'W')
+         AND (cd_education_status = '2 yr Degree')
+         AND (ss_sales_price BETWEEN 150.00 AND 200.00)
+         AND (hd_dep_count = 1)))
+   AND (((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('TX'      , 'OH'      , 'TX'))
+         AND (ss_net_profit BETWEEN 100 AND 200))
+      OR ((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('OR'      , 'NM'      , 'KY'))
+         AND (ss_net_profit BETWEEN 150 AND 300))
+      OR ((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('VA'      , 'TX'      , 'MS'))
+         AND (ss_net_profit BETWEEN 50 AND 250)))
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.sql
new file mode 100644
index 0000000000..3108b3e56e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_1.sql
@@ -0,0 +1,165 @@
+WITH
+  cross_items AS (
+   SELECT i_item_sk ss_item_sk
+   FROM
+     item
+   , (
+      SELECT
+        iss.i_brand_id brand_id
+      , iss.i_class_id class_id
+      , iss.i_category_id category_id
+      FROM
+        store_sales
+      , item iss
+      , date_dim d1
+      WHERE (ss_item_sk = iss.i_item_sk)
+         AND (ss_sold_date_sk = d1.d_date_sk)
+         AND (d1.d_year BETWEEN 1999 AND (1999 + 2))
+INTERSECT       SELECT
+        ics.i_brand_id
+      , ics.i_class_id
+      , ics.i_category_id
+      FROM
+        catalog_sales
+      , item ics
+      , date_dim d2
+      WHERE (cs_item_sk = ics.i_item_sk)
+         AND (cs_sold_date_sk = d2.d_date_sk)
+         AND (d2.d_year BETWEEN 1999 AND (1999 + 2))
+INTERSECT       SELECT
+        iws.i_brand_id
+      , iws.i_class_id
+      , iws.i_category_id
+      FROM
+        web_sales
+      , item iws
+      , date_dim d3
+      WHERE (ws_item_sk = iws.i_item_sk)
+         AND (ws_sold_date_sk = d3.d_date_sk)
+         AND (d3.d_year BETWEEN 1999 AND (1999 + 2))
+   ) y 
+   WHERE (i_brand_id = brand_id)
+      AND (i_class_id = class_id)
+      AND (i_category_id = category_id)
+)
+, avg_sales AS (
+   SELECT avg((quantity * list_price)) average_sales
+   FROM
+     (
+      SELECT
+        ss_quantity quantity
+      , ss_list_price list_price
+      FROM
+        store_sales
+      , date_dim
+      WHERE (ss_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+UNION ALL       SELECT
+        cs_quantity quantity
+      , cs_list_price list_price
+      FROM
+        catalog_sales
+      , date_dim
+      WHERE (cs_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+UNION ALL       SELECT
+        ws_quantity quantity
+      , ws_list_price list_price
+      FROM
+        web_sales
+      , date_dim
+      WHERE (ws_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+   )  x
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  channel
+, i_brand_id
+, i_class_id
+, i_category_id
+, sum(sales)
+, sum(number_sales)
+FROM
+  (
+   SELECT
+     'store' channel
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , sum((ss_quantity * ss_list_price)) sales
+   , count(*) number_sales
+   FROM
+     store_sales
+   , item
+   , date_dim
+   WHERE (ss_item_sk IN (
+      SELECT ss_item_sk
+      FROM
+        cross_items
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = (1999 + 2))
+      AND (d_moy = 11)
+   GROUP BY i_brand_id, i_class_id, i_category_id
+   HAVING (sum((ss_quantity * ss_list_price)) > (
+         SELECT average_sales
+         FROM
+           avg_sales
+      ))
+UNION ALL    SELECT
+     'catalog' channel
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , sum((cs_quantity * cs_list_price)) sales
+   , count(*) number_sales
+   FROM
+     catalog_sales
+   , item
+   , date_dim
+   WHERE (cs_item_sk IN (
+      SELECT ss_item_sk
+      FROM
+        cross_items
+   ))
+      AND (cs_item_sk = i_item_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (d_year = (1999 + 2))
+      AND (d_moy = 11)
+   GROUP BY i_brand_id, i_class_id, i_category_id
+   HAVING (sum((cs_quantity * cs_list_price)) > (
+         SELECT average_sales
+         FROM
+           avg_sales
+      ))
+UNION ALL    SELECT
+     'web' channel
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , sum((ws_quantity * ws_list_price)) sales
+   , count(*) number_sales
+   FROM
+     web_sales
+   , item
+   , date_dim
+   WHERE (ws_item_sk IN (
+      SELECT ss_item_sk
+      FROM
+        cross_items
+   ))
+      AND (ws_item_sk = i_item_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year = (1999 + 2))
+      AND (d_moy = 11)
+   GROUP BY i_brand_id, i_class_id, i_category_id
+   HAVING (sum((ws_quantity * ws_list_price)) > (
+         SELECT average_sales
+         FROM
+           avg_sales
+      ))
+)  y
+GROUP BY ROLLUP (channel, i_brand_id, i_class_id, i_category_id)
+ORDER BY channel ASC, i_brand_id ASC, i_class_id ASC, i_category_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_2.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_2.sql
new file mode 100644
index 0000000000..307fef9d25
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q14_2.sql
@@ -0,0 +1,149 @@
+WITH
+  cross_items AS (
+   SELECT i_item_sk ss_item_sk
+   FROM
+     item
+   , (
+      SELECT
+        iss.i_brand_id brand_id
+      , iss.i_class_id class_id
+      , iss.i_category_id category_id
+      FROM
+        store_sales
+      , item iss
+      , date_dim d1
+      WHERE (ss_item_sk = iss.i_item_sk)
+         AND (ss_sold_date_sk = d1.d_date_sk)
+         AND (d1.d_year BETWEEN 1999 AND (1999 + 2))
+INTERSECT       SELECT
+        ics.i_brand_id
+      , ics.i_class_id
+      , ics.i_category_id
+      FROM
+        catalog_sales
+      , item ics
+      , date_dim d2
+      WHERE (cs_item_sk = ics.i_item_sk)
+         AND (cs_sold_date_sk = d2.d_date_sk)
+         AND (d2.d_year BETWEEN 1999 AND (1999 + 2))
+INTERSECT       SELECT
+        iws.i_brand_id
+      , iws.i_class_id
+      , iws.i_category_id
+      FROM
+        web_sales
+      , item iws
+      , date_dim d3
+      WHERE (ws_item_sk = iws.i_item_sk)
+         AND (ws_sold_date_sk = d3.d_date_sk)
+         AND (d3.d_year BETWEEN 1999 AND (1999 + 2))
+   )  x
+   WHERE (i_brand_id = brand_id)
+      AND (i_class_id = class_id)
+      AND (i_category_id = category_id)
+)
+, avg_sales AS (
+   SELECT avg((quantity * list_price)) average_sales
+   FROM
+     (
+      SELECT
+        ss_quantity quantity
+      , ss_list_price list_price
+      FROM
+        store_sales
+      , date_dim
+      WHERE (ss_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+UNION ALL       SELECT
+        cs_quantity quantity
+      , cs_list_price list_price
+      FROM
+        catalog_sales
+      , date_dim
+      WHERE (cs_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+UNION ALL       SELECT
+        ws_quantity quantity
+      , ws_list_price list_price
+      FROM
+        web_sales
+      , date_dim
+      WHERE (ws_sold_date_sk = d_date_sk)
+         AND (d_year BETWEEN 1999 AND (1999 + 2))
+   ) y
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     'store' channel
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , sum((ss_quantity * ss_list_price)) sales
+   , count(*) number_sales
+   FROM
+     store_sales
+   , item
+   , date_dim
+   WHERE (ss_item_sk IN (
+      SELECT ss_item_sk
+      FROM
+        cross_items
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_week_seq = (
+         SELECT d_week_seq
+         FROM
+           date_dim
+         WHERE (d_year = (1999 + 1))
+            AND (d_moy = 12)
+            AND (d_dom = 11)
+      ))
+   GROUP BY i_brand_id, i_class_id, i_category_id
+   HAVING (sum((ss_quantity * ss_list_price)) > (
+         SELECT average_sales
+         FROM
+           avg_sales
+      ))
+)  this_year
+, (
+   SELECT
+     'store' channel
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , sum((ss_quantity * ss_list_price)) sales
+   , count(*) number_sales
+   FROM
+     store_sales
+   , item
+   , date_dim
+   WHERE (ss_item_sk IN (
+      SELECT ss_item_sk
+      FROM
+        cross_items
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_week_seq = (
+         SELECT d_week_seq
+         FROM
+           date_dim
+         WHERE (d_year = 1999)
+            AND (d_moy = 12)
+            AND (d_dom = 11)
+      ))
+   GROUP BY i_brand_id, i_class_id, i_category_id
+   HAVING (sum((ss_quantity * ss_list_price)) > (
+         SELECT average_sales
+         FROM
+           avg_sales
+      ))
+)  last_year
+WHERE (this_year.i_brand_id = last_year.i_brand_id)
+   AND (this_year.i_class_id = last_year.i_class_id)
+   AND (this_year.i_category_id = last_year.i_category_id)
+ORDER BY this_year.channel ASC, this_year.i_brand_id ASC, this_year.i_class_id ASC, this_year.i_category_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q15.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q15.sql
new file mode 100644
index 0000000000..2ca5e11f9d
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q15.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  ca_zip
+, sum(cs_sales_price)
+FROM
+  catalog_sales
+, customer
+, customer_address
+, date_dim
+WHERE (cs_bill_customer_sk = c_customer_sk)
+   AND (c_current_addr_sk = ca_address_sk)
+   AND ((substr(ca_zip, 1, 5) IN ('85669'   , '86197'   , '88274'   , '83405'   , '86475'   , '85392'   , '85460'   , '80348'   , '81792'))
+      OR (ca_state IN ('CA'   , 'WA'   , 'GA'))
+      OR (cs_sales_price > 500))
+   AND (cs_sold_date_sk = d_date_sk)
+   AND (d_qoy = 2)
+   AND (d_year = 2001)
+GROUP BY ca_zip
+ORDER BY ca_zip ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q16.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q16.sql
new file mode 100644
index 0000000000..30bcb76dc9
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q16.sql
@@ -0,0 +1,30 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  count(DISTINCT cs_order_number) 'order count'
+, sum(cs_ext_ship_cost) 'total shipping cost'
+, sum(cs_net_profit) 'total net profit'
+FROM
+  catalog_sales cs1
+, date_dim
+, customer_address
+, call_center
+WHERE (d_date BETWEEN CAST('2002-2-01' AS DATE) AND (CAST('2002-2-01' AS DATE) + INTERVAL  '60' DAY))
+   AND (cs1.cs_ship_date_sk = d_date_sk)
+   AND (cs1.cs_ship_addr_sk = ca_address_sk)
+   AND (ca_state = 'GA')
+   AND (cs1.cs_call_center_sk = cc_call_center_sk)
+   AND (cc_county IN ('Williamson County', 'Williamson County', 'Williamson County', 'Williamson County', 'Williamson County'))
+   AND (EXISTS (
+   SELECT *
+   FROM
+     catalog_sales cs2
+   WHERE (cs1.cs_order_number = cs2.cs_order_number)
+      AND (cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)
+))
+   AND (NOT (EXISTS (
+   SELECT *
+   FROM
+     catalog_returns cr1
+   WHERE (cs1.cs_order_number = cr1.cr_order_number)
+)))
+ORDER BY count(DISTINCT cs_order_number) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q17.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q17.sql
new file mode 100644
index 0000000000..cdda9c6310
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q17.sql
@@ -0,0 +1,41 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, s_state
+, count(ss_quantity) store_sales_quantitycount
+, avg(ss_quantity) store_sales_quantityave
+, stddev_samp(ss_quantity) store_sales_quantitystdev
+, (stddev_samp(ss_quantity) / avg(ss_quantity)) store_sales_quantitycov
+, count(sr_return_quantity) store_returns_quantitycount
+, avg(sr_return_quantity) store_returns_quantityave
+, stddev_samp(sr_return_quantity) store_returns_quantitystdev
+, (stddev_samp(sr_return_quantity) / avg(sr_return_quantity)) store_returns_quantitycov
+, count(cs_quantity) catalog_sales_quantitycount
+, avg(cs_quantity) catalog_sales_quantityave
+, stddev_samp(cs_quantity) catalog_sales_quantitystdev
+, (stddev_samp(cs_quantity) / avg(cs_quantity)) catalog_sales_quantitycov
+FROM
+  store_sales
+, store_returns
+, catalog_sales
+, date_dim d1
+, date_dim d2
+, date_dim d3
+, store
+, item
+WHERE (d1.d_quarter_name = '2001Q1')
+   AND (d1.d_date_sk = ss_sold_date_sk)
+   AND (i_item_sk = ss_item_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (ss_customer_sk = sr_customer_sk)
+   AND (ss_item_sk = sr_item_sk)
+   AND (ss_ticket_number = sr_ticket_number)
+   AND (sr_returned_date_sk = d2.d_date_sk)
+   AND (d2.d_quarter_name IN ('2001Q1', '2001Q2', '2001Q3'))
+   AND (sr_customer_sk = cs_bill_customer_sk)
+   AND (sr_item_sk = cs_item_sk)
+   AND (cs_sold_date_sk = d3.d_date_sk)
+   AND (d3.d_quarter_name IN ('2001Q1', '2001Q2', '2001Q3'))
+GROUP BY i_item_id, i_item_desc, s_state
+ORDER BY i_item_id ASC, i_item_desc ASC, s_state ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q18.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q18.sql
new file mode 100644
index 0000000000..8c2eb2f4cf
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q18.sql
@@ -0,0 +1,34 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, ca_country
+, ca_state
+, ca_county
+, avg(CAST(cs_quantity AS DECIMALV3(12,2))) agg1
+, avg(CAST(cs_list_price AS DECIMALV3(12,2))) agg2
+, avg(CAST(cs_coupon_amt AS DECIMALV3(12,2))) agg3
+, avg(CAST(cs_sales_price AS DECIMALV3(12,2))) agg4
+, avg(CAST(cs_net_profit AS DECIMALV3(12,2))) agg5
+, avg(CAST(c_birth_year AS DECIMALV3(12,2))) agg6
+, avg(CAST(cd1.cd_dep_count AS DECIMALV3(12,2))) agg7
+FROM
+  catalog_sales
+, customer_demographics cd1
+, customer_demographics cd2
+, customer
+, customer_address
+, date_dim
+, item
+WHERE (cs_sold_date_sk = d_date_sk)
+   AND (cs_item_sk = i_item_sk)
+   AND (cs_bill_cdemo_sk = cd1.cd_demo_sk)
+   AND (cs_bill_customer_sk = c_customer_sk)
+   AND (cd1.cd_gender = 'F')
+   AND (cd1.cd_education_status = 'Unknown')
+   AND (c_current_cdemo_sk = cd2.cd_demo_sk)
+   AND (c_current_addr_sk = ca_address_sk)
+   AND (c_birth_month IN (1, 6, 8, 9, 12, 2))
+   AND (d_year = 1998)
+   AND (ca_state IN ('MS', 'IN', 'ND', 'OK', 'NM', 'VA', 'MS'))
+GROUP BY ROLLUP (i_item_id, ca_country, ca_state, ca_county)
+ORDER BY ca_country ASC, ca_state ASC, ca_county ASC, i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q19.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q19.sql
new file mode 100644
index 0000000000..5606e71f6e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q19.sql
@@ -0,0 +1,25 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_brand_id brand_id
+, i_brand brand
+, i_manufact_id
+, i_manufact
+, sum(ss_ext_sales_price) ext_price
+FROM
+  date_dim
+, store_sales
+, item
+, customer
+, customer_address
+, store
+WHERE (d_date_sk = ss_sold_date_sk)
+   AND (ss_item_sk = i_item_sk)
+   AND (i_manager_id = 8)
+   AND (d_moy = 11)
+   AND (d_year = 1998)
+   AND (ss_customer_sk = c_customer_sk)
+   AND (c_current_addr_sk = ca_address_sk)
+   AND (substr(ca_zip, 1, 5) <> substr(s_zip, 1, 5))
+   AND (ss_store_sk = s_store_sk)
+GROUP BY i_brand, i_brand_id, i_manufact_id, i_manufact
+ORDER BY ext_price DESC, i_brand ASC, i_brand_id ASC, i_manufact_id ASC, i_manufact ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q20.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q20.sql
new file mode 100644
index 0000000000..58c0e65767
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q20.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, i_category
+, i_class
+, i_current_price
+, sum(cs_ext_sales_price) itemrevenue
+, ((sum(cs_ext_sales_price) * 100) / sum(sum(cs_ext_sales_price)) OVER (PARTITION BY i_class)) revenueratio
+FROM
+  catalog_sales
+, item
+, date_dim
+WHERE (cs_item_sk = i_item_sk)
+   AND (i_category IN ('Sports', 'Books', 'Home'))
+   AND (cs_sold_date_sk = d_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN CAST('1999-02-22' AS DATE) AND (CAST('1999-02-22' AS DATE) + INTERVAL  '30' DAY))
+GROUP BY i_item_id, i_item_desc, i_category, i_class, i_current_price
+ORDER BY i_category ASC, i_class ASC, i_item_id ASC, i_item_desc ASC, revenueratio ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q21.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q21.sql
new file mode 100644
index 0000000000..30fdbd03ea
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q21.sql
@@ -0,0 +1,23 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     w_warehouse_name
+   , i_item_id
+   , sum((CASE WHEN (CAST(d_date AS DATE) < CAST('2000-03-11' AS DATE)) THEN inv_quantity_on_hand ELSE 0 END)) inv_before
+   , sum((CASE WHEN (CAST(d_date AS DATE) >= CAST('2000-03-11' AS DATE)) THEN inv_quantity_on_hand ELSE 0 END)) inv_after
+   FROM
+     inventory
+   , warehouse
+   , item
+   , date_dim
+   WHERE (i_current_price BETWEEN 0.99 AND 1.49)
+      AND (i_item_sk = inv_item_sk)
+      AND (inv_warehouse_sk = w_warehouse_sk)
+      AND (inv_date_sk = d_date_sk)
+      AND (d_date BETWEEN (CAST('2000-03-11' AS DATE) - INTERVAL  '30' DAY) AND (CAST('2000-03-11' AS DATE) + INTERVAL  '30' DAY))
+   GROUP BY w_warehouse_name, i_item_id
+)  x
+WHERE ((CASE WHEN (inv_before > 0) THEN (inv_after / inv_before) ELSE null END) BETWEEN (2.00 / 3.00) AND (3.00 / 2.00))
+ORDER BY w_warehouse_name ASC, i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q22.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q22.sql
new file mode 100644
index 0000000000..6a310bc8ec
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q22.sql
@@ -0,0 +1,16 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_product_name
+, i_brand
+, i_class
+, i_category
+, avg(inv_quantity_on_hand) qoh
+FROM
+  inventory
+, date_dim
+, item
+WHERE (inv_date_sk = d_date_sk)
+   AND (inv_item_sk = i_item_sk)
+   AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+GROUP BY ROLLUP (i_product_name, i_brand, i_class, i_category)
+ORDER BY qoh ASC, i_product_name ASC, i_brand ASC, i_class ASC, i_category ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.sql
new file mode 100644
index 0000000000..2ddb472171
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_1.sql
@@ -0,0 +1,88 @@
+WITH
+  frequent_ss_items AS (
+   SELECT
+     substr(i_item_desc, 1, 30) itemdesc
+   , i_item_sk item_sk
+   , d_date solddate
+   , count(*) cnt
+   FROM
+     store_sales
+   , date_dim
+   , item
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (d_year IN (2000   , (2000 + 1)   , (2000 + 2)   , (2000 + 3)))
+   GROUP BY substr(i_item_desc, 1, 30), i_item_sk, d_date
+   HAVING (count(*) > 4)
+)
+, max_store_sales AS (
+   SELECT max(csales) tpcds_cmax
+   FROM
+     (
+      SELECT
+        c_customer_sk
+      , sum((ss_quantity * ss_sales_price)) csales
+      FROM
+        store_sales
+      , customer
+      , date_dim
+      WHERE (ss_customer_sk = c_customer_sk)
+         AND (ss_sold_date_sk = d_date_sk)
+         AND (d_year IN (2000      , (2000 + 1)      , (2000 + 2)      , (2000 + 3)))
+      GROUP BY c_customer_sk
+   ) x
+)
+, best_ss_customer AS (
+   SELECT
+     c_customer_sk
+   , sum((ss_quantity * ss_sales_price)) ssales
+   FROM
+     store_sales
+   , customer
+   WHERE (ss_customer_sk = c_customer_sk)
+   GROUP BY c_customer_sk
+   HAVING (sum((ss_quantity * ss_sales_price)) > ((50 / 100.0) * (
+            SELECT *
+            FROM
+              max_store_sales
+         )))
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ sum(sales)
+FROM
+  (
+   SELECT (cs_quantity * cs_list_price) sales
+   FROM
+     catalog_sales
+   , date_dim
+   WHERE (d_year = 2000)
+      AND (d_moy = 2)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (cs_item_sk IN (
+      SELECT item_sk
+      FROM
+        frequent_ss_items
+   ))
+      AND (cs_bill_customer_sk IN (
+      SELECT c_customer_sk
+      FROM
+        best_ss_customer
+   ))
+UNION ALL    SELECT (ws_quantity * ws_list_price) sales
+   FROM
+     web_sales
+   , date_dim
+   WHERE (d_year = 2000)
+      AND (d_moy = 2)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (ws_item_sk IN (
+      SELECT item_sk
+      FROM
+        frequent_ss_items
+   ))
+      AND (ws_bill_customer_sk IN (
+      SELECT c_customer_sk
+      FROM
+        best_ss_customer
+   ))
+) y
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.sql
new file mode 100644
index 0000000000..d1ee516c0e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q23_2.sql
@@ -0,0 +1,104 @@
+WITH
+  frequent_ss_items AS (
+   SELECT
+     substr(i_item_desc, 1, 30) itemdesc
+   , i_item_sk item_sk
+   , d_date solddate
+   , count(*) cnt
+   FROM
+     store_sales
+   , date_dim
+   , item
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (d_year IN (2000   , (2000 + 1)   , (2000 + 2)   , (2000 + 3)))
+   GROUP BY substr(i_item_desc, 1, 30), i_item_sk, d_date
+   HAVING (count(*) > 4)
+)
+, max_store_sales AS (
+   SELECT max(csales) tpcds_cmax
+   FROM
+     (
+      SELECT
+        c_customer_sk
+      , sum((ss_quantity * ss_sales_price)) csales
+      FROM
+        store_sales
+      , customer
+      , date_dim
+      WHERE (ss_customer_sk = c_customer_sk)
+         AND (ss_sold_date_sk = d_date_sk)
+         AND (d_year IN (2000      , (2000 + 1)      , (2000 + 2)      , (2000 + 3)))
+      GROUP BY c_customer_sk
+   ) x
+)
+, best_ss_customer AS (
+   SELECT
+     c_customer_sk
+   , sum((ss_quantity * ss_sales_price)) ssales
+   FROM
+     store_sales
+   , customer
+   WHERE (ss_customer_sk = c_customer_sk)
+   GROUP BY c_customer_sk
+   HAVING (sum((ss_quantity * ss_sales_price)) > ((50 / 100.0) * (
+            SELECT *
+            FROM
+              max_store_sales
+         )))
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, sales
+FROM
+  (
+   SELECT
+     c_last_name
+   , c_first_name
+   , sum((cs_quantity * cs_list_price)) sales
+   FROM
+     catalog_sales
+   , customer
+   , date_dim
+   WHERE (d_year = 2000)
+      AND (d_moy = 2)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (cs_item_sk IN (
+      SELECT item_sk
+      FROM
+        frequent_ss_items
+   ))
+      AND (cs_bill_customer_sk IN (
+      SELECT c_customer_sk
+      FROM
+        best_ss_customer
+   ))
+      AND (cs_bill_customer_sk = c_customer_sk)
+   GROUP BY c_last_name, c_first_name
+UNION ALL    SELECT
+     c_last_name
+   , c_first_name
+   , sum((ws_quantity * ws_list_price)) sales
+   FROM
+     web_sales
+   , customer
+   , date_dim
+   WHERE (d_year = 2000)
+      AND (d_moy = 2)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (ws_item_sk IN (
+      SELECT item_sk
+      FROM
+        frequent_ss_items
+   ))
+      AND (ws_bill_customer_sk IN (
+      SELECT c_customer_sk
+      FROM
+        best_ss_customer
+   ))
+      AND (ws_bill_customer_sk = c_customer_sk)
+   GROUP BY c_last_name, c_first_name
+) z 
+ORDER BY c_last_name ASC, c_first_name ASC, sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.sql
new file mode 100644
index 0000000000..03c2c226d0
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_1.sql
@@ -0,0 +1,46 @@
+WITH
+  ssales AS (
+   SELECT
+     c_last_name
+   , c_first_name
+   , s_store_name
+   , ca_state
+   , s_state
+   , i_color
+   , i_current_price
+   , i_manager_id
+   , i_units
+   , i_size
+   , sum(ss_net_paid) netpaid
+   FROM
+     store_sales
+   , store_returns
+   , store
+   , item
+   , customer
+   , customer_address
+   WHERE (ss_ticket_number = sr_ticket_number)
+      AND (ss_item_sk = sr_item_sk)
+      AND (ss_customer_sk = c_customer_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (c_birth_country = upper(ca_country))
+      AND (s_zip = ca_zip)
+      AND (s_market_id = 8)
+   GROUP BY c_last_name, c_first_name, s_store_name, ca_state, s_state, i_color, i_current_price, i_manager_id, i_units, i_size
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, s_store_name
+, sum(netpaid) paid
+FROM
+  ssales
+WHERE (i_color = 'pale')
+GROUP BY c_last_name, c_first_name, s_store_name
+HAVING (sum(netpaid) > (
+      SELECT (0.05 * avg(netpaid))
+      FROM
+        ssales
+   ))
+ORDER BY c_last_name, c_first_name, s_store_name
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.sql
new file mode 100644
index 0000000000..5c1e3b182b
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q24_2.sql
@@ -0,0 +1,46 @@
+WITH
+  ssales AS (
+   SELECT
+     c_last_name
+   , c_first_name
+   , s_store_name
+   , ca_state
+   , s_state
+   , i_color
+   , i_current_price
+   , i_manager_id
+   , i_units
+   , i_size
+   , sum(ss_net_paid) netpaid
+   FROM
+     store_sales
+   , store_returns
+   , store
+   , item
+   , customer
+   , customer_address
+   WHERE (ss_ticket_number = sr_ticket_number)
+      AND (ss_item_sk = sr_item_sk)
+      AND (ss_customer_sk = c_customer_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (c_birth_country = upper(ca_country))
+      AND (s_zip = ca_zip)
+      AND (s_market_id = 8)
+   GROUP BY c_last_name, c_first_name, s_store_name, ca_state, s_state, i_color, i_current_price, i_manager_id, i_units, i_size
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, s_store_name
+, sum(netpaid) paid
+FROM
+  ssales
+WHERE (i_color = 'chiffon')
+GROUP BY c_last_name, c_first_name, s_store_name
+HAVING (sum(netpaid) > (
+      SELECT (0.05 * avg(netpaid))
+      FROM
+        ssales
+   ))
+ORDER BY c_last_name, c_first_name, s_store_name
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q25.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q25.sql
new file mode 100644
index 0000000000..ffae7246c4
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q25.sql
@@ -0,0 +1,36 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, s_store_id
+, s_store_name
+, sum(ss_net_profit) store_sales_profit
+, sum(sr_net_loss) store_returns_loss
+, sum(cs_net_profit) catalog_sales_profit
+FROM
+  store_sales
+, store_returns
+, catalog_sales
+, date_dim d1
+, date_dim d2
+, date_dim d3
+, store
+, item
+WHERE (d1.d_moy = 4)
+   AND (d1.d_year = 2001)
+   AND (d1.d_date_sk = ss_sold_date_sk)
+   AND (i_item_sk = ss_item_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (ss_customer_sk = sr_customer_sk)
+   AND (ss_item_sk = sr_item_sk)
+   AND (ss_ticket_number = sr_ticket_number)
+   AND (sr_returned_date_sk = d2.d_date_sk)
+   AND (d2.d_moy BETWEEN 4 AND 10)
+   AND (d2.d_year = 2001)
+   AND (sr_customer_sk = cs_bill_customer_sk)
+   AND (sr_item_sk = cs_item_sk)
+   AND (cs_sold_date_sk = d3.d_date_sk)
+   AND (d3.d_moy BETWEEN 4 AND 10)
+   AND (d3.d_year = 2001)
+GROUP BY i_item_id, i_item_desc, s_store_id, s_store_name
+ORDER BY i_item_id ASC, i_item_desc ASC, s_store_id ASC, s_store_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q26.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q26.sql
new file mode 100644
index 0000000000..13e9ff27a9
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q26.sql
@@ -0,0 +1,25 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, avg(cs_quantity) agg1
+, avg(cs_list_price) agg2
+, avg(cs_coupon_amt) agg3
+, avg(cs_sales_price) agg4
+FROM
+  catalog_sales
+, customer_demographics
+, date_dim
+, item
+, promotion
+WHERE (cs_sold_date_sk = d_date_sk)
+   AND (cs_item_sk = i_item_sk)
+   AND (cs_bill_cdemo_sk = cd_demo_sk)
+   AND (cs_promo_sk = p_promo_sk)
+   AND (cd_gender = 'M')
+   AND (cd_marital_status = 'S')
+   AND (cd_education_status = 'College')
+   AND ((p_channel_email = 'N')
+      OR (p_channel_event = 'N'))
+   AND (d_year = 2000)
+GROUP BY i_item_id
+ORDER BY i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q27.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q27.sql
new file mode 100644
index 0000000000..e6efac3175
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q27.sql
@@ -0,0 +1,32 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, s_state
+, GROUPING (s_state) g_state
+, avg(ss_quantity) agg1
+, avg(ss_list_price) agg2
+, avg(ss_coupon_amt) agg3
+, avg(ss_sales_price) agg4
+FROM
+  store_sales
+, customer_demographics
+, date_dim
+, store
+, item
+WHERE (ss_sold_date_sk = d_date_sk)
+   AND (ss_item_sk = i_item_sk)
+   AND (ss_store_sk = s_store_sk)
+   AND (ss_cdemo_sk = cd_demo_sk)
+   AND (cd_gender = 'M')
+   AND (cd_marital_status = 'S')
+   AND (cd_education_status = 'College')
+   AND (d_year = 2002)
+   AND (s_state IN (
+     'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'))
+GROUP BY ROLLUP (i_item_id, s_state)
+ORDER BY i_item_id ASC, s_state ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q28.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q28.sql
new file mode 100644
index 0000000000..5eecd64069
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q28.sql
@@ -0,0 +1,75 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     avg(ss_list_price) b1_lp
+   , count(ss_list_price) b1_cnt
+   , count(DISTINCT ss_list_price) b1_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 0 AND 5)
+      AND ((ss_list_price BETWEEN 8 AND (8 + 10))
+         OR (ss_coupon_amt BETWEEN 459 AND (459 + 1000))
+         OR (ss_wholesale_cost BETWEEN 57 AND (57 + 20)))
+)  b1
+, (
+   SELECT
+     avg(ss_list_price) b2_lp
+   , count(ss_list_price) b2_cnt
+   , count(DISTINCT ss_list_price) b2_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 6 AND 10)
+      AND ((ss_list_price BETWEEN 90 AND (90 + 10))
+         OR (ss_coupon_amt BETWEEN 2323 AND (2323 + 1000))
+         OR (ss_wholesale_cost BETWEEN 31 AND (31 + 20)))
+)  b2
+, (
+   SELECT
+     avg(ss_list_price) b3_lp
+   , count(ss_list_price) b3_cnt
+   , count(DISTINCT ss_list_price) b3_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 11 AND 15)
+      AND ((ss_list_price BETWEEN 142 AND (142 + 10))
+         OR (ss_coupon_amt BETWEEN 12214 AND (12214 + 1000))
+         OR (ss_wholesale_cost BETWEEN 79 AND (79 + 20)))
+)  b3
+, (
+   SELECT
+     avg(ss_list_price) b4_lp
+   , count(ss_list_price) b4_cnt
+   , count(DISTINCT ss_list_price) b4_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 16 AND 20)
+      AND ((ss_list_price BETWEEN 135 AND (135 + 10))
+         OR (ss_coupon_amt BETWEEN 6071 AND (6071 + 1000))
+         OR (ss_wholesale_cost BETWEEN 38 AND (38 + 20)))
+)  b4
+, (
+   SELECT
+     avg(ss_list_price) b5_lp
+   , count(ss_list_price) b5_cnt
+   , count(DISTINCT ss_list_price) b5_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 21 AND 25)
+      AND ((ss_list_price BETWEEN 122 AND (122 + 10))
+         OR (ss_coupon_amt BETWEEN 836 AND (836 + 1000))
+         OR (ss_wholesale_cost BETWEEN 17 AND (17 + 20)))
+)  b5
+, (
+   SELECT
+     avg(ss_list_price) b6_lp
+   , count(ss_list_price) b6_cnt
+   , count(DISTINCT ss_list_price) b6_cntd
+   FROM
+     store_sales
+   WHERE (ss_quantity BETWEEN 26 AND 30)
+      AND ((ss_list_price BETWEEN 154 AND (154 + 10))
+         OR (ss_coupon_amt BETWEEN 7326 AND (7326 + 1000))
+         OR (ss_wholesale_cost BETWEEN 7 AND (7 + 20)))
+)  b6
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q29.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q29.sql
new file mode 100644
index 0000000000..6134304058
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q29.sql
@@ -0,0 +1,35 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, s_store_id
+, s_store_name
+, sum(ss_quantity) store_sales_quantity
+, sum(sr_return_quantity) store_returns_quantity
+, sum(cs_quantity) catalog_sales_quantity
+FROM
+  store_sales
+, store_returns
+, catalog_sales
+, date_dim d1
+, date_dim d2
+, date_dim d3
+, store
+, item
+WHERE (d1.d_moy = 9)
+   AND (d1.d_year = 1999)
+   AND (d1.d_date_sk = ss_sold_date_sk)
+   AND (i_item_sk = ss_item_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (ss_customer_sk = sr_customer_sk)
+   AND (ss_item_sk = sr_item_sk)
+   AND (ss_ticket_number = sr_ticket_number)
+   AND (sr_returned_date_sk = d2.d_date_sk)
+   AND (d2.d_moy BETWEEN 9 AND (9 + 3))
+   AND (d2.d_year = 1999)
+   AND (sr_customer_sk = cs_bill_customer_sk)
+   AND (sr_item_sk = cs_item_sk)
+   AND (cs_sold_date_sk = d3.d_date_sk)
+   AND (d3.d_year IN (1999, (1999 + 1), (1999 + 2)))
+GROUP BY i_item_id, i_item_desc, s_store_id, s_store_name
+ORDER BY i_item_id ASC, i_item_desc ASC, s_store_id ASC, s_store_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q30.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q30.sql
new file mode 100644
index 0000000000..03780ec12b
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q30.sql
@@ -0,0 +1,49 @@
+-- Expect cell is: C??TE D'IVOIRE
+-- But real is: C?TE D'IVOIRE
+-- compare result fail when encounter latin character Ô
+/*
+WITH
+  customer_total_return AS (
+   SELECT
+     wr_returning_customer_sk ctr_customer_sk
+   , ca_state ctr_state
+   , sum(wr_return_amt) ctr_total_return
+   FROM
+     web_returns
+   , date_dim
+   , customer_address
+   WHERE (wr_returned_date_sk = d_date_sk)
+      AND (d_year = 2002)
+      AND (wr_returning_addr_sk = ca_address_sk)
+   GROUP BY wr_returning_customer_sk, ca_state
+)
+SELECT
+  c_customer_id
+, c_salutation
+, c_first_name
+, c_last_name
+, c_preferred_cust_flag
+, c_birth_day
+, c_birth_month
+, c_birth_year
+, c_birth_country
+, c_login
+, c_email_address
+, c_last_review_date_sk
+, ctr_total_return
+FROM
+  customer_total_return ctr1
+, customer_address
+, customer
+WHERE (ctr1.ctr_total_return > (
+      SELECT (avg(ctr_total_return) * 1.2)
+      FROM
+        customer_total_return ctr2
+      WHERE (ctr1.ctr_state = ctr2.ctr_state)
+   ))
+   AND (ca_address_sk = c_current_addr_sk)
+   AND (ca_state = 'GA')
+   AND (ctr1.ctr_customer_sk = c_customer_sk)
+ORDER BY c_customer_id ASC, c_salutation ASC, c_first_name ASC, c_last_name ASC, c_preferred_cust_flag ASC, c_birth_day ASC, c_birth_month ASC, c_birth_year ASC, c_birth_country ASC, c_login ASC, c_email_address ASC, c_last_review_date_sk ASC, ctr_total_return ASC
+LIMIT 100
+*/
\ No newline at end of file
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q31.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q31.sql
new file mode 100644
index 0000000000..cce695917a
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q31.sql
@@ -0,0 +1,63 @@
+WITH
+  ss AS (
+   SELECT
+     ca_county
+   , d_qoy
+   , d_year
+   , sum(ss_ext_sales_price) store_sales
+   FROM
+     store_sales
+   , date_dim
+   , customer_address
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (ss_addr_sk = ca_address_sk)
+   GROUP BY ca_county, d_qoy, d_year
+)
+, ws AS (
+   SELECT
+     ca_county
+   , d_qoy
+   , d_year
+   , sum(ws_ext_sales_price) web_sales
+   FROM
+     web_sales
+   , date_dim
+   , customer_address
+   WHERE (ws_sold_date_sk = d_date_sk)
+      AND (ws_bill_addr_sk = ca_address_sk)
+   GROUP BY ca_county, d_qoy, d_year
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  ss1.ca_county
+, ss1.d_year
+, (ws2.web_sales / ws1.web_sales) web_q1_q2_increase
+, (ss2.store_sales / ss1.store_sales) store_q1_q2_increase
+, (ws3.web_sales / ws2.web_sales) web_q2_q3_increase
+, (ss3.store_sales / ss2.store_sales) store_q2_q3_increase
+FROM
+  ss ss1
+, ss ss2
+, ss ss3
+, ws ws1
+, ws ws2
+, ws ws3
+WHERE (ss1.d_qoy = 1)
+   AND (ss1.d_year = 2000)
+   AND (ss1.ca_county = ss2.ca_county)
+   AND (ss2.d_qoy = 2)
+   AND (ss2.d_year = 2000)
+   AND (ss2.ca_county = ss3.ca_county)
+   AND (ss3.d_qoy = 3)
+   AND (ss3.d_year = 2000)
+   AND (ss1.ca_county = ws1.ca_county)
+   AND (ws1.d_qoy = 1)
+   AND (ws1.d_year = 2000)
+   AND (ws1.ca_county = ws2.ca_county)
+   AND (ws2.d_qoy = 2)
+   AND (ws2.d_year = 2000)
+   AND (ws1.ca_county = ws3.ca_county)
+   AND (ws3.d_qoy = 3)
+   AND (ws3.d_year = 2000)
+   AND ((CASE WHEN (ws1.web_sales > 0) THEN (ws2.web_sales / ws1.web_sales) ELSE null END) > (CASE WHEN (ss1.store_sales > 0) THEN (ss2.store_sales / ss1.store_sales) ELSE null END))
+   AND ((CASE WHEN (ws2.web_sales > 0) THEN (ws3.web_sales / ws2.web_sales) ELSE null END) > (CASE WHEN (ss2.store_sales > 0) THEN (ss3.store_sales / ss2.store_sales) ELSE null END))
+ORDER BY ss1.ca_county ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q32.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q32.sql
new file mode 100644
index 0000000000..200a0ff320
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q32.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ sum(cs_ext_discount_amt) 'excess discount amount'
+FROM
+  catalog_sales
+, item
+, date_dim
+WHERE (i_manufact_id = 977)
+   AND (i_item_sk = cs_item_sk)
+   AND (d_date BETWEEN CAST('2000-01-27' AS DATE) AND (CAST('2000-01-27' AS DATE) + INTERVAL  '90' DAY))
+   AND (d_date_sk = cs_sold_date_sk)
+   AND (cs_ext_discount_amt > (
+      SELECT (1.3 * avg(cs_ext_discount_amt))
+      FROM
+        catalog_sales
+      , date_dim
+      WHERE (cs_item_sk = i_item_sk)
+         AND (d_date BETWEEN CAST('2000-01-27' AS DATE) AND (CAST('2000-01-27' AS DATE) + INTERVAL  '90' DAY))
+         AND (d_date_sk = cs_sold_date_sk)
+   ))
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q33.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q33.sql
new file mode 100644
index 0000000000..14deee0997
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q33.sql
@@ -0,0 +1,88 @@
+WITH
+  ss AS (
+   SELECT
+     i_manufact_id
+   , sum(ss_ext_sales_price) total_sales
+   FROM
+     store_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_manufact_id IN (
+      SELECT i_manufact_id
+      FROM
+        item
+      WHERE (i_category IN ('Electronics'))
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 5)
+      AND (ss_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_manufact_id
+)
+, cs AS (
+   SELECT
+     i_manufact_id
+   , sum(cs_ext_sales_price) total_sales
+   FROM
+     catalog_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_manufact_id IN (
+      SELECT i_manufact_id
+      FROM
+        item
+      WHERE (i_category IN ('Electronics'))
+   ))
+      AND (cs_item_sk = i_item_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 5)
+      AND (cs_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_manufact_id
+)
+, ws AS (
+   SELECT
+     i_manufact_id
+   , sum(ws_ext_sales_price) total_sales
+   FROM
+     web_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_manufact_id IN (
+      SELECT i_manufact_id
+      FROM
+        item
+      WHERE (i_category IN ('Electronics'))
+   ))
+      AND (ws_item_sk = i_item_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 5)
+      AND (ws_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_manufact_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_manufact_id
+, sum(total_sales) total_sales
+FROM
+  (
+   SELECT *
+   FROM
+     ss
+UNION ALL    SELECT *
+   FROM
+     cs
+UNION ALL    SELECT *
+   FROM
+     ws
+)  tmp1
+GROUP BY i_manufact_id
+ORDER BY total_sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q34.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q34.sql
new file mode 100644
index 0000000000..d8e4631ad8
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q34.sql
@@ -0,0 +1,35 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, c_salutation
+, c_preferred_cust_flag
+, ss_ticket_number
+, cnt
+FROM
+  (
+   SELECT
+     ss_ticket_number
+   , ss_customer_sk
+   , count(*) cnt
+   FROM
+     store_sales
+   , date_dim
+   , store
+   , household_demographics
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_store_sk = store.s_store_sk)
+      AND (store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND ((date_dim.d_dom BETWEEN 1 AND 3)
+         OR (date_dim.d_dom BETWEEN 25 AND 28))
+      AND ((household_demographics.hd_buy_potential = '>10000')
+         OR (household_demographics.hd_buy_potential = 'Unknown'))
+      AND (household_demographics.hd_vehicle_count > 0)
+      AND ((CASE WHEN (household_demographics.hd_vehicle_count > 0) THEN (household_demographics.hd_dep_count / household_demographics.hd_vehicle_count) ELSE null END) > 1.2)
+      AND (date_dim.d_year IN (1999   , (1999 + 1)   , (1999 + 2)))
+      AND (store.s_county IN ('Williamson County'   , 'Williamson County'   , 'Williamson County'   , 'Williamson County'   , 'Williamson County'   , 'Williamson County'   , 'Williamson County'   , 'Williamson County'))
+   GROUP BY ss_ticket_number, ss_customer_sk
+)  dn
+, customer
+WHERE (ss_customer_sk = c_customer_sk)
+   AND (cnt BETWEEN 15 AND 20)
+ORDER BY c_last_name ASC, c_first_name ASC, c_salutation ASC, c_preferred_cust_flag DESC, ss_ticket_number ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q35.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q35.sql
new file mode 100644
index 0000000000..1466c41c22
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q35.sql
@@ -0,0 +1,60 @@
+/*
+SELECT
+  ca_state
+, cd_gender
+, cd_marital_status
+, cd_dep_count
+, count(*) cnt1
+, min(cd_dep_count)
+, max(cd_dep_count)
+, avg(cd_dep_count)
+, cd_dep_employed_count
+, count(*) cnt2
+, min(cd_dep_employed_count)
+, max(cd_dep_employed_count)
+, avg(cd_dep_employed_count)
+, cd_dep_college_count
+, count(*) cnt3
+, min(cd_dep_college_count)
+, max(cd_dep_college_count)
+, avg(cd_dep_college_count)
+FROM
+  customer c
+, customer_address ca
+, customer_demographics
+WHERE (c.c_current_addr_sk = ca.ca_address_sk)
+   AND (cd_demo_sk = c.c_current_cdemo_sk)
+   AND (EXISTS (
+   SELECT *
+   FROM
+     store_sales
+   , date_dim
+   WHERE (c.c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 2002)
+      AND (d_qoy < 4)
+))
+   AND ((EXISTS (
+      SELECT *
+      FROM
+        web_sales
+      , date_dim
+      WHERE (c.c_customer_sk = ws_bill_customer_sk)
+         AND (ws_sold_date_sk = d_date_sk)
+         AND (d_year = 2002)
+         AND (d_qoy < 4)
+   ))
+      OR (EXISTS (
+      SELECT *
+      FROM
+        catalog_sales
+      , date_dim
+      WHERE (c.c_customer_sk = cs_ship_customer_sk)
+         AND (cs_sold_date_sk = d_date_sk)
+         AND (d_year = 2002)
+         AND (d_qoy < 4)
+   )))
+GROUP BY ca_state, cd_gender, cd_marital_status, cd_dep_count, cd_dep_employed_count, cd_dep_college_count
+ORDER BY ca_state ASC, cd_gender ASC, cd_marital_status ASC, cd_dep_count ASC, cd_dep_employed_count ASC, cd_dep_college_count ASC
+LIMIT 100
+*/
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q36.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q36.sql
new file mode 100644
index 0000000000..10a26ae9d3
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q36.sql
@@ -0,0 +1,27 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  (sum(ss_net_profit) / sum(ss_ext_sales_price)) gross_margin
+, i_category
+, i_class
+, (GROUPING (i_category) + GROUPING (i_class)) lochierarchy
+, rank() OVER (PARTITION BY (GROUPING (i_category) + GROUPING (i_class)), (CASE WHEN (GROUPING (i_class) = 0) THEN i_category END) ORDER BY (sum(ss_net_profit) / sum(ss_ext_sales_price)) ASC) rank_within_parent
+FROM
+  store_sales
+, date_dim d1
+, item
+, store
+WHERE (d1.d_year = 2001)
+   AND (d1.d_date_sk = ss_sold_date_sk)
+   AND (i_item_sk = ss_item_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (s_state IN (
+     'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'
+   , 'TN'))
+GROUP BY ROLLUP (i_category, i_class)
+ORDER BY lochierarchy DESC, (CASE WHEN (lochierarchy = 0) THEN i_category END) ASC, rank_within_parent ASC, i_category, i_class
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q37.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q37.sql
new file mode 100644
index 0000000000..77e8736082
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q37.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, i_current_price
+FROM
+  item
+, inventory
+, date_dim
+, catalog_sales
+WHERE (i_current_price BETWEEN 68 AND (68 + 30))
+   AND (inv_item_sk = i_item_sk)
+   AND (d_date_sk = inv_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN CAST('2000-02-01' AS DATE) AND (CAST('2000-02-01' AS DATE) + INTERVAL  '60' DAY))
+   AND (i_manufact_id IN (677, 940, 694, 808))
+   AND (inv_quantity_on_hand BETWEEN 100 AND 500)
+   AND (cs_item_sk = i_item_sk)
+GROUP BY i_item_id, i_item_desc, i_current_price
+ORDER BY i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q38.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q38.sql
new file mode 100644
index 0000000000..3d7ab41ea8
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q38.sql
@@ -0,0 +1,38 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ count(*)
+FROM
+  (
+   SELECT DISTINCT
+     c_last_name
+   , c_first_name
+   , d_date
+   FROM
+     store_sales
+   , date_dim
+   , customer
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_customer_sk = customer.c_customer_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+INTERSECT    SELECT DISTINCT
+     c_last_name
+   , c_first_name
+   , d_date
+   FROM
+     catalog_sales
+   , date_dim
+   , customer
+   WHERE (catalog_sales.cs_sold_date_sk = date_dim.d_date_sk)
+      AND (catalog_sales.cs_bill_customer_sk = customer.c_customer_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+INTERSECT    SELECT DISTINCT
+     c_last_name
+   , c_first_name
+   , d_date
+   FROM
+     web_sales
+   , date_dim
+   , customer
+   WHERE (web_sales.ws_sold_date_sk = date_dim.d_date_sk)
+      AND (web_sales.ws_bill_customer_sk = customer.c_customer_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+)  hot_cust
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.sql
new file mode 100644
index 0000000000..65dabcd0ae
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_1.sql
@@ -0,0 +1,51 @@
+WITH
+  inv AS (
+   SELECT
+     w_warehouse_name
+   , w_warehouse_sk
+   , i_item_sk
+   , d_moy
+   , stdev
+   , mean
+   , (CASE mean WHEN 0 THEN null ELSE (stdev / mean) END) cov
+   FROM
+     (
+      SELECT
+        w_warehouse_name
+      , w_warehouse_sk
+      , i_item_sk
+      , d_moy
+      , stddev_samp(inv_quantity_on_hand) stdev
+      , avg(inv_quantity_on_hand) mean
+      FROM
+        inventory
+      , item
+      , warehouse
+      , date_dim
+      WHERE (inv_item_sk = i_item_sk)
+         AND (inv_warehouse_sk = w_warehouse_sk)
+         AND (inv_date_sk = d_date_sk)
+         AND (d_year = 2001)
+      GROUP BY w_warehouse_name, w_warehouse_sk, i_item_sk, d_moy
+   )  foo
+   WHERE ((CASE mean WHEN 0 THEN 0 ELSE (stdev / mean) END) > 1)
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  inv1.w_warehouse_sk
+, inv1.i_item_sk
+, inv1.d_moy
+, inv1.mean
+, inv1.cov
+, inv2.w_warehouse_sk
+, inv2.i_item_sk
+, inv2.d_moy
+, inv2.mean
+, inv2.cov
+FROM
+  inv inv1
+, inv inv2
+WHERE (inv1.i_item_sk = inv2.i_item_sk)
+   AND (inv1.w_warehouse_sk = inv2.w_warehouse_sk)
+   AND (inv1.d_moy = 1)
+   AND (inv2.d_moy = (1 + 1))
+ORDER BY inv1.w_warehouse_sk ASC, inv1.i_item_sk ASC, inv1.d_moy ASC, inv1.mean ASC, inv1.cov ASC, inv2.d_moy ASC, inv2.mean ASC, inv2.cov ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.sql
new file mode 100644
index 0000000000..2adb4676e9
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q39_2.sql
@@ -0,0 +1,52 @@
+WITH
+  inv AS (
+   SELECT
+     w_warehouse_name
+   , w_warehouse_sk
+   , i_item_sk
+   , d_moy
+   , stdev
+   , mean
+   , (CASE mean WHEN 0 THEN null ELSE (stdev / mean) END) cov
+   FROM
+     (
+      SELECT
+        w_warehouse_name
+      , w_warehouse_sk
+      , i_item_sk
+      , d_moy
+      , stddev_samp(inv_quantity_on_hand) stdev
+      , avg(inv_quantity_on_hand) mean
+      FROM
+        inventory
+      , item
+      , warehouse
+      , date_dim
+      WHERE (inv_item_sk = i_item_sk)
+         AND (inv_warehouse_sk = w_warehouse_sk)
+         AND (inv_date_sk = d_date_sk)
+         AND (d_year = 2001)
+      GROUP BY w_warehouse_name, w_warehouse_sk, i_item_sk, d_moy
+   )  foo
+   WHERE ((CASE mean WHEN 0 THEN 0 ELSE (stdev / mean) END) > 1)
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  inv1.w_warehouse_sk
+, inv1.i_item_sk
+, inv1.d_moy
+, inv1.mean
+, inv1.cov
+, inv2.w_warehouse_sk
+, inv2.i_item_sk
+, inv2.d_moy
+, inv2.mean
+, inv2.cov
+FROM
+  inv inv1
+, inv inv2
+WHERE (inv1.i_item_sk = inv2.i_item_sk)
+   AND (inv1.w_warehouse_sk = inv2.w_warehouse_sk)
+   AND (inv1.d_moy = 1)
+   AND (inv2.d_moy = (1 + 1))
+   AND (inv1.cov > 1.5)
+ORDER BY inv1.w_warehouse_sk ASC, inv1.i_item_sk ASC, inv1.d_moy ASC, inv1.mean ASC, inv1.cov ASC, inv2.d_moy ASC, inv2.mean ASC, inv2.cov ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q40.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q40.sql
new file mode 100644
index 0000000000..b0ea3a22b6
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q40.sql
@@ -0,0 +1,20 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  w_state
+, i_item_id
+, sum((CASE WHEN (CAST(d_date AS DATE) < CAST('2000-03-11' AS DATE)) THEN (cs_sales_price - COALESCE(cr_refunded_cash, 0)) ELSE 0 END)) sales_before
+, sum((CASE WHEN (CAST(d_date AS DATE) >= CAST('2000-03-11' AS DATE)) THEN (cs_sales_price - COALESCE(cr_refunded_cash, 0)) ELSE 0 END)) sales_after
+FROM
+  catalog_sales
+LEFT JOIN catalog_returns ON (cs_order_number = cr_order_number)
+   AND (cs_item_sk = cr_item_sk)
+, warehouse
+, item
+, date_dim
+WHERE (i_current_price BETWEEN 0.99 AND 1.49)
+   AND (i_item_sk = cs_item_sk)
+   AND (cs_warehouse_sk = w_warehouse_sk)
+   AND (cs_sold_date_sk = d_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN (CAST('2000-03-11' AS DATE) - INTERVAL  '30' DAY) AND (CAST('2000-03-11' AS DATE) + INTERVAL  '30' DAY))
+GROUP BY w_state, i_item_id
+ORDER BY w_state ASC, i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q41.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q41.sql
new file mode 100644
index 0000000000..c8d3e6fbf8
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q41.sql
@@ -0,0 +1,69 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ DISTINCT i_product_name
+FROM
+  item i1
+WHERE (i_manufact_id BETWEEN 738 AND (738 + 40))
+   AND ((
+      SELECT count(*) item_cnt
+      FROM
+        item
+      WHERE ((i_manufact = i1.i_manufact)
+            AND (((i_category = 'Women')
+                  AND ((i_color = 'powder')
+                     OR (i_color = 'khaki'))
+                  AND ((i_units = 'Ounce')
+                     OR (i_units = 'Oz'))
+                  AND ((i_size = 'medium')
+                     OR (i_size = 'extra large')))
+               OR ((i_category = 'Women')
+                  AND ((i_color = 'brown')
+                     OR (i_color = 'honeydew'))
+                  AND ((i_units = 'Bunch')
+                     OR (i_units = 'Ton'))
+                  AND ((i_size = 'N/A')
+                     OR (i_size = 'small')))
+               OR ((i_category = 'Men')
+                  AND ((i_color = 'floral')
+                     OR (i_color = 'deep'))
+                  AND ((i_units = 'N/A')
+                     OR (i_units = 'Dozen'))
+                  AND ((i_size = 'petite')
+                     OR (i_size = 'large')))
+               OR ((i_category = 'Men')
+                  AND ((i_color = 'light')
+                     OR (i_color = 'cornflower'))
+                  AND ((i_units = 'Box')
+                     OR (i_units = 'Pound'))
+                  AND ((i_size = 'medium')
+                     OR (i_size = 'extra large')))))
+         OR ((i_manufact = i1.i_manufact)
+            AND (((i_category = 'Women')
+                  AND ((i_color = 'midnight')
+                     OR (i_color = 'snow'))
+                  AND ((i_units = 'Pallet')
+                     OR (i_units = 'Gross'))
+                  AND ((i_size = 'medium')
+                     OR (i_size = 'extra large')))
+               OR ((i_category = 'Women')
+                  AND ((i_color = 'cyan')
+                     OR (i_color = 'papaya'))
+                  AND ((i_units = 'Cup')
+                     OR (i_units = 'Dram'))
+                  AND ((i_size = 'N/A')
+                     OR (i_size = 'small')))
+               OR ((i_category = 'Men')
+                  AND ((i_color = 'orange')
+                     OR (i_color = 'frosted'))
+                  AND ((i_units = 'Each')
+                     OR (i_units = 'Tbl'))
+                  AND ((i_size = 'petite')
+                     OR (i_size = 'large')))
+               OR ((i_category = 'Men')
+                  AND ((i_color = 'forest')
+                     OR (i_color = 'ghost'))
+                  AND ((i_units = 'Lb')
+                     OR (i_units = 'Bundle'))
+                  AND ((i_size = 'medium')
+                     OR (i_size = 'extra large')))))
+   ) > 0)
+ORDER BY i_product_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q42.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q42.sql
new file mode 100644
index 0000000000..0376725abc
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q42.sql
@@ -0,0 +1,17 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  dt.d_year
+, item.i_category_id
+, item.i_category
+, sum(ss_ext_sales_price)
+FROM
+  date_dim dt
+, store_sales
+, item
+WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
+   AND (store_sales.ss_item_sk = item.i_item_sk)
+   AND (item.i_manager_id = 1)
+   AND (dt.d_moy = 11)
+   AND (dt.d_year = 2000)
+GROUP BY dt.d_year, item.i_category_id, item.i_category
+ORDER BY sum(ss_ext_sales_price) DESC, dt.d_year ASC, item.i_category_id ASC, item.i_category ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q43.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q43.sql
new file mode 100644
index 0000000000..4fb67212ba
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q43.sql
@@ -0,0 +1,21 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  s_store_name
+, s_store_id
+, sum((CASE WHEN (d_day_name = 'Sunday') THEN ss_sales_price ELSE null END)) sun_sales
+, sum((CASE WHEN (d_day_name = 'Monday') THEN ss_sales_price ELSE null END)) mon_sales
+, sum((CASE WHEN (d_day_name = 'Tuesday') THEN ss_sales_price ELSE null END)) tue_sales
+, sum((CASE WHEN (d_day_name = 'Wednesday') THEN ss_sales_price ELSE null END)) wed_sales
+, sum((CASE WHEN (d_day_name = 'Thursday') THEN ss_sales_price ELSE null END)) thu_sales
+, sum((CASE WHEN (d_day_name = 'Friday') THEN ss_sales_price ELSE null END)) fri_sales
+, sum((CASE WHEN (d_day_name = 'Saturday') THEN ss_sales_price ELSE null END)) sat_sales
+FROM
+  date_dim
+, store_sales
+, store
+WHERE (d_date_sk = ss_sold_date_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (s_gmt_offset = -5)
+   AND (d_year = 2000)
+GROUP BY s_store_name, s_store_id
+ORDER BY s_store_name ASC, s_store_id ASC, sun_sales ASC, mon_sales ASC, tue_sales ASC, wed_sales ASC, thu_sales ASC, fri_sales ASC, sat_sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q44.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q44.sql
new file mode 100644
index 0000000000..c4be8bd19a
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q44.sql
@@ -0,0 +1,68 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  asceding.rnk
+, i1.i_product_name best_performing
+, i2.i_product_name worst_performing
+FROM
+  (
+   SELECT *
+   FROM
+     (
+      SELECT
+        item_sk
+      , rank() OVER (ORDER BY rank_col ASC) rnk
+      FROM
+        (
+         SELECT
+           ss_item_sk item_sk
+         , avg(ss_net_profit) rank_col
+         FROM
+           store_sales ss1
+         WHERE (ss_store_sk = 4)
+         GROUP BY ss_item_sk
+         HAVING (avg(ss_net_profit) > (0.9 * (
+                  SELECT avg(ss_net_profit) rank_col
+                  FROM
+                    store_sales
+                  WHERE (ss_store_sk = 4)
+                     AND (ss_addr_sk IS NULL)
+                  GROUP BY ss_store_sk
+               )))
+      )  v1
+   )  v11
+   WHERE (rnk < 11)
+)  asceding
+, (
+   SELECT *
+   FROM
+     (
+      SELECT
+        item_sk
+      , rank() OVER (ORDER BY rank_col DESC) rnk
+      FROM
+        (
+         SELECT
+           ss_item_sk item_sk
+         , avg(ss_net_profit) rank_col
+         FROM
+           store_sales ss1
+         WHERE (ss_store_sk = 4)
+         GROUP BY ss_item_sk
+         HAVING (avg(ss_net_profit) > (CAST('0.9' AS DECIMAL) * (
+                  SELECT avg(ss_net_profit) rank_col
+                  FROM
+                    store_sales
+                  WHERE (ss_store_sk = 4)
+                     AND (ss_addr_sk IS NULL)
+                  GROUP BY ss_store_sk
+               )))
+      )  v2
+   )  v21
+   WHERE (rnk < 11)
+)  descending
+, item i1
+, item i2
+WHERE (asceding.rnk = descending.rnk)
+   AND (i1.i_item_sk = asceding.item_sk)
+   AND (i2.i_item_sk = descending.item_sk)
+ORDER BY asceding.rnk ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q45.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q45.sql
new file mode 100644
index 0000000000..fe5bf82ef8
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q45.sql
@@ -0,0 +1,28 @@
+/*
+SELECT
+  ca_zip
+, ca_city
+, sum(ws_sales_price)
+FROM
+  web_sales
+, customer
+, customer_address
+, date_dim
+, item
+WHERE (ws_bill_customer_sk = c_customer_sk)
+   AND (c_current_addr_sk = ca_address_sk)
+   AND (ws_item_sk = i_item_sk)
+   AND ((substr(ca_zip, 1, 5) IN ('85669'   , '86197'   , '88274'   , '83405'   , '86475'   , '85392'   , '85460'   , '80348'   , '81792'))
+      OR (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_item_sk IN (2      , 3      , 5      , 7      , 11      , 13      , 17      , 19      , 23      , 29))
+   )))
+   AND (ws_sold_date_sk = d_date_sk)
+   AND (d_qoy = 2)
+   AND (d_year = 2001)
+GROUP BY ca_zip, ca_city
+ORDER BY ca_zip ASC, ca_city ASC
+LIMIT 100
+*/
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q46.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q46.sql
new file mode 100644
index 0000000000..e18b69a140
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q46.sql
@@ -0,0 +1,40 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, ca_city
+, bought_city
+, ss_ticket_number
+, amt
+, profit
+FROM
+  (
+   SELECT
+     ss_ticket_number
+   , ss_customer_sk
+   , ca_city bought_city
+   , sum(ss_coupon_amt) amt
+   , sum(ss_net_profit) profit
+   FROM
+     store_sales
+   , date_dim
+   , store
+   , household_demographics
+   , customer_address
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_store_sk = store.s_store_sk)
+      AND (store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (store_sales.ss_addr_sk = customer_address.ca_address_sk)
+      AND ((household_demographics.hd_dep_count = 4)
+         OR (household_demographics.hd_vehicle_count = 3))
+      AND (date_dim.d_dow IN (6   , 0))
+      AND (date_dim.d_year IN (1999   , (1999 + 1)   , (1999 + 2)))
+      AND (store.s_city IN ('Fairview'   , 'Midway'   , 'Fairview'   , 'Fairview'   , 'Fairview'))
+   GROUP BY ss_ticket_number, ss_customer_sk, ss_addr_sk, ca_city
+)  dn
+, customer
+, customer_address current_addr
+WHERE (ss_customer_sk = c_customer_sk)
+   AND (customer.c_current_addr_sk = current_addr.ca_address_sk)
+   AND (current_addr.ca_city <> bought_city)
+ORDER BY c_last_name ASC, c_first_name ASC, ca_city ASC, bought_city ASC, ss_ticket_number ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q47.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q47.sql
new file mode 100644
index 0000000000..95df467d6a
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q47.sql
@@ -0,0 +1,62 @@
+WITH
+  v1 AS (
+   SELECT
+     i_category
+   , i_brand
+   , s_store_name
+   , s_company_name
+   , d_year
+   , d_moy
+   , sum(ss_sales_price) sum_sales
+   , avg(sum(ss_sales_price)) OVER (PARTITION BY i_category, i_brand, s_store_name, s_company_name, d_year) avg_monthly_sales
+   , rank() OVER (PARTITION BY i_category, i_brand, s_store_name, s_company_name ORDER BY d_year ASC, d_moy ASC) rn
+   FROM
+     item
+   , store_sales
+   , date_dim
+   , store
+   WHERE (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND ((d_year = 1999)
+         OR ((d_year = (1999 - 1))
+            AND (d_moy = 12))
+         OR ((d_year = (1999 + 1))
+            AND (d_moy = 1)))
+   GROUP BY i_category, i_brand, s_store_name, s_company_name, d_year, d_moy
+)
+, v2 AS (
+   SELECT
+     v1.i_category
+   , v1.i_brand
+   , v1.s_store_name
+   , v1.s_company_name
+   , v1.d_year
+   , v1.d_moy
+   , v1.avg_monthly_sales
+   , v1.sum_sales
+   , v1_lag.sum_sales psum
+   , v1_lead.sum_sales nsum
+   FROM
+     v1
+   , v1 v1_lag
+   , v1 v1_lead
+   WHERE (v1.i_category = v1_lag.i_category)
+      AND (v1.i_category = v1_lead.i_category)
+      AND (v1.i_brand = v1_lag.i_brand)
+      AND (v1.i_brand = v1_lead.i_brand)
+      AND (v1.s_store_name = v1_lag.s_store_name)
+      AND (v1.s_store_name = v1_lead.s_store_name)
+      AND (v1.s_company_name = v1_lag.s_company_name)
+      AND (v1.s_company_name = v1_lead.s_company_name)
+      AND (v1.rn = (v1_lag.rn + 1))
+      AND (v1.rn = (v1_lead.rn - 1))
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  v2
+WHERE (d_year = 1999)
+   AND (avg_monthly_sales > 0)
+   AND ((CASE WHEN (avg_monthly_sales > 0) THEN (abs((sum_sales - avg_monthly_sales)) / avg_monthly_sales) ELSE null END) > 0.1)
+ORDER BY (sum_sales - avg_monthly_sales) ASC, 3 ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q48.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q48.sql
new file mode 100644
index 0000000000..ae4ad6e808
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q48.sql
@@ -0,0 +1,34 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ sum(ss_quantity)
+FROM
+  store_sales
+, store
+, customer_demographics
+, customer_address
+, date_dim
+WHERE (s_store_sk = ss_store_sk)
+   AND (ss_sold_date_sk = d_date_sk)
+   AND (d_year = 2000)
+   AND (((cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'M')
+         AND (cd_education_status = '4 yr Degree')
+         AND (ss_sales_price BETWEEN 100.00 AND 150.00))
+      OR ((cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'D')
+         AND (cd_education_status = '2 yr Degree')
+         AND (ss_sales_price BETWEEN 50.00 AND 100.00))
+      OR ((cd_demo_sk = ss_cdemo_sk)
+         AND (cd_marital_status = 'S')
+         AND (cd_education_status = 'College')
+         AND (ss_sales_price BETWEEN 150.00 AND 200.00)))
+   AND (((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('CO'      , 'OH'      , 'TX'))
+         AND (ss_net_profit BETWEEN 0 AND 2000))
+      OR ((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('OR'      , 'MN'      , 'KY'))
+         AND (ss_net_profit BETWEEN 150 AND 3000))
+      OR ((ss_addr_sk = ca_address_sk)
+         AND (ca_country = 'United States')
+         AND (ca_state IN ('VA'      , 'CA'      , 'MS'))
+         AND (ss_net_profit BETWEEN 50 AND 25000)))
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49.sql
new file mode 100644
index 0000000000..dbff634bfa
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49.sql
@@ -0,0 +1,118 @@
+-- 目前union后跟得order没有效果，需要在外面包一层select后再order
+
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ channel, item, return_ratio, return_rank, currency_rank
+FROM
+(SELECT
+  'web' channel
+, web.item
+, web.return_ratio
+, web.return_rank
+, web.currency_rank
+FROM
+  (
+   SELECT
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        ws.ws_item_sk item
+      , (CAST(sum(COALESCE(wr.wr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(ws.ws_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(wr.wr_return_amt, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(ws.ws_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        web_sales ws
+      LEFT JOIN web_returns wr ON (ws.ws_order_number = wr.wr_order_number)
+         AND (ws.ws_item_sk = wr.wr_item_sk)
+      , date_dim
+      WHERE (wr.wr_return_amt > 10000)
+         AND (ws.ws_net_profit > 1)
+         AND (ws.ws_net_paid > 0)
+         AND (ws.ws_quantity > 0)
+         AND (ws_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY ws.ws_item_sk
+   )  in_web
+)  web
+WHERE (web.return_rank <= 10)
+   OR (web.currency_rank <= 10)
+UNION SELECT
+  'catalog' channel
+, catalog.item
+, catalog.return_ratio
+, catalog.return_rank
+, catalog.currency_rank
+FROM
+  (
+   SELECT
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        cs.cs_item_sk item
+      , (CAST(sum(COALESCE(cr.cr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(cs.cs_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(cr.cr_return_amount, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(cs.cs_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        catalog_sales cs
+      LEFT JOIN catalog_returns cr ON (cs.cs_order_number = cr.cr_order_number)
+         AND (cs.cs_item_sk = cr.cr_item_sk)
+      , date_dim
+      WHERE (cr.cr_return_amount > 10000)
+         AND (cs.cs_net_profit > 1)
+         AND (cs.cs_net_paid > 0)
+         AND (cs.cs_quantity > 0)
+         AND (cs_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY cs.cs_item_sk
+   )  in_cat
+) catalog 
+WHERE (catalog.return_rank <= 10)
+   OR (catalog.currency_rank <= 10)
+UNION SELECT
+  'store' channel
+, store.item
+, store.return_ratio
+, store.return_rank
+, store.currency_rank
+FROM
+  (
+   SELECT
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        sts.ss_item_sk item
+      , (CAST(sum(COALESCE(sr.sr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(sts.ss_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(sr.sr_return_amt, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(sts.ss_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        store_sales sts
+      LEFT JOIN store_returns sr ON (sts.ss_ticket_number = sr.sr_ticket_number)
+         AND (sts.ss_item_sk = sr.sr_item_sk)
+      , date_dim
+      WHERE (sr.sr_return_amt > 10000)
+         AND (sts.ss_net_profit > 1)
+         AND (sts.ss_net_paid > 0)
+         AND (sts.ss_quantity > 0)
+         AND (ss_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY sts.ss_item_sk
+   )  in_store
+)  store
+WHERE (store.return_rank <= 10)
+   OR (store.currency_rank <= 10)
+) r
+ORDER BY 1 ASC, 4 ASC, 5 ASC, 2 ASC
+LIMIT 100;
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.sql
new file mode 100644
index 0000000000..edd644dffc
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q49_rewrite.sql
@@ -0,0 +1,113 @@
+(SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  'web' channel
+, web.item
+, web.return_ratio
+, web.return_rank
+, web.currency_rank
+FROM
+  (
+   SELECT
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        ws.ws_item_sk item
+      , (CAST(sum(COALESCE(wr.wr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(ws.ws_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(wr.wr_return_amt, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(ws.ws_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        web_sales ws
+      LEFT JOIN web_returns wr ON (ws.ws_order_number = wr.wr_order_number)
+         AND (ws.ws_item_sk = wr.wr_item_sk)
+      , date_dim
+      WHERE (wr.wr_return_amt > 10000)
+         AND (ws.ws_net_profit > 1)
+         AND (ws.ws_net_paid > 0)
+         AND (ws.ws_quantity > 0)
+         AND (ws_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY ws.ws_item_sk
+   )  in_web
+)  web
+WHERE (web.return_rank <= 10)
+   OR (web.currency_rank <= 10))
+UNION (SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  'catalog' channel
+, catalog.item
+, catalog.return_ratio
+, catalog.return_rank
+, catalog.currency_rank
+FROM
+  (
+   SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        cs.cs_item_sk item
+      , (CAST(sum(COALESCE(cr.cr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(cs.cs_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(cr.cr_return_amount, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(cs.cs_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        catalog_sales cs
+      LEFT JOIN catalog_returns cr ON (cs.cs_order_number = cr.cr_order_number)
+         AND (cs.cs_item_sk = cr.cr_item_sk)
+      , date_dim
+      WHERE (cr.cr_return_amount > 10000)
+         AND (cs.cs_net_profit > 1)
+         AND (cs.cs_net_paid > 0)
+         AND (cs.cs_quantity > 0)
+         AND (cs_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY cs.cs_item_sk
+   )  in_cat
+) catalog 
+WHERE (catalog.return_rank <= 10)
+   OR (catalog.currency_rank <= 10))
+UNION (SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  'store' channel
+, store.item
+, store.return_ratio
+, store.return_rank
+, store.currency_rank
+FROM
+  (
+   SELECT
+     item
+   , return_ratio
+   , currency_ratio
+   , rank() OVER (ORDER BY return_ratio ASC) return_rank
+   , rank() OVER (ORDER BY currency_ratio ASC) currency_rank
+   FROM
+     (
+      SELECT
+        sts.ss_item_sk item
+      , (CAST(sum(COALESCE(sr.sr_return_quantity, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(sts.ss_quantity, 0)) AS DECIMALV3(15,4))) return_ratio
+      , (CAST(sum(COALESCE(sr.sr_return_amt, 0)) AS DECIMALV3(15,4)) / CAST(sum(COALESCE(sts.ss_net_paid, 0)) AS DECIMALV3(15,4))) currency_ratio
+      FROM
+        store_sales sts
+      LEFT JOIN store_returns sr ON (sts.ss_ticket_number = sr.sr_ticket_number)
+         AND (sts.ss_item_sk = sr.sr_item_sk)
+      , date_dim
+      WHERE (sr.sr_return_amt > 10000)
+         AND (sts.ss_net_profit > 1)
+         AND (sts.ss_net_paid > 0)
+         AND (sts.ss_quantity > 0)
+         AND (ss_sold_date_sk = d_date_sk)
+         AND (d_year = 2001)
+         AND (d_moy = 12)
+      GROUP BY sts.ss_item_sk
+   )  in_store
+)  store
+WHERE (store.return_rank <= 10)
+   OR (store.currency_rank <= 10))
+ORDER BY 1 ASC, 4 ASC, 5 ASC, 2 ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q50.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q50.sql
new file mode 100644
index 0000000000..da07d05965
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q50.sql
@@ -0,0 +1,36 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  s_store_name
+, s_company_id
+, s_street_number
+, s_street_name
+, s_street_type
+, s_suite_number
+, s_city
+, s_county
+, s_state
+, s_zip
+, sum((CASE WHEN ((sr_returned_date_sk - ss_sold_date_sk) <= 30) THEN 1 ELSE 0 END)) '30 days'
+, sum((CASE WHEN ((sr_returned_date_sk - ss_sold_date_sk) > 30)
+   AND ((sr_returned_date_sk - ss_sold_date_sk) <= 60) THEN 1 ELSE 0 END)) '31-60 days'
+, sum((CASE WHEN ((sr_returned_date_sk - ss_sold_date_sk) > 60)
+   AND ((sr_returned_date_sk - ss_sold_date_sk) <= 90) THEN 1 ELSE 0 END)) '61-90 days'
+, sum((CASE WHEN ((sr_returned_date_sk - ss_sold_date_sk) > 90)
+   AND ((sr_returned_date_sk - ss_sold_date_sk) <= 120) THEN 1 ELSE 0 END)) '91-120 days'
+, sum((CASE WHEN ((sr_returned_date_sk - ss_sold_date_sk) > 120) THEN 1 ELSE 0 END)) '>120 days'
+FROM
+  store_sales
+, store_returns
+, store
+, date_dim d1
+, date_dim d2
+WHERE (d2.d_year = 2001)
+   AND (d2.d_moy = 8)
+   AND (ss_ticket_number = sr_ticket_number)
+   AND (ss_item_sk = sr_item_sk)
+   AND (ss_sold_date_sk = d1.d_date_sk)
+   AND (sr_returned_date_sk = d2.d_date_sk)
+   AND (ss_customer_sk = sr_customer_sk)
+   AND (ss_store_sk = s_store_sk)
+GROUP BY s_store_name, s_company_id, s_street_number, s_street_name, s_street_type, s_suite_number, s_city, s_county, s_state, s_zip
+ORDER BY s_store_name ASC, s_company_id ASC, s_street_number ASC, s_street_name ASC, s_street_type ASC, s_suite_number ASC, s_city ASC, s_county ASC, s_state ASC, s_zip ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q51.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q51.sql
new file mode 100644
index 0000000000..0eb0cb5d26
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q51.sql
@@ -0,0 +1,53 @@
+WITH
+  web_v1 AS (
+   SELECT
+     ws_item_sk item_sk
+   , d_date
+   , sum(sum(ws_sales_price)) OVER (PARTITION BY ws_item_sk ORDER BY d_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) cume_sales
+   FROM
+     web_sales
+   , date_dim
+   WHERE (ws_sold_date_sk = d_date_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+      AND (ws_item_sk IS NOT NULL)
+   GROUP BY ws_item_sk, d_date
+)
+, store_v1 AS (
+   SELECT
+     ss_item_sk item_sk
+   , d_date
+   , sum(sum(ss_sales_price)) OVER (PARTITION BY ss_item_sk ORDER BY d_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) cume_sales
+   FROM
+     store_sales
+   , date_dim
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+      AND (ss_item_sk IS NOT NULL)
+   GROUP BY ss_item_sk, d_date
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     item_sk
+   , d_date
+   , web_sales
+   , store_sales
+   , max(web_sales) OVER (PARTITION BY item_sk ORDER BY d_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) web_cumulative
+   , max(store_sales) OVER (PARTITION BY item_sk ORDER BY d_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) store_cumulative
+   FROM
+     (
+      SELECT
+        (CASE WHEN (web.item_sk IS NOT NULL) THEN web.item_sk ELSE store.item_sk END) item_sk
+      , (CASE WHEN (web.d_date IS NOT NULL) THEN web.d_date ELSE store.d_date END) d_date
+      , web.cume_sales web_sales
+      , store.cume_sales store_sales
+      FROM
+        web_v1 web
+      FULL JOIN store_v1 store ON (web.item_sk = store.item_sk)
+         AND (web.d_date = store.d_date)
+   )  x
+)  y
+WHERE (web_cumulative > store_cumulative)
+ORDER BY item_sk ASC, d_date ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q52.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q52.sql
new file mode 100644
index 0000000000..7a5000e4c3
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q52.sql
@@ -0,0 +1,17 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  dt.d_year
+, item.i_brand_id brand_id
+, item.i_brand brand
+, sum(ss_ext_sales_price) ext_price
+FROM
+  date_dim dt
+, store_sales
+, item
+WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
+   AND (store_sales.ss_item_sk = item.i_item_sk)
+   AND (item.i_manager_id = 1)
+   AND (dt.d_moy = 11)
+   AND (dt.d_year = 2000)
+GROUP BY dt.d_year, item.i_brand, item.i_brand_id
+ORDER BY dt.d_year ASC, ext_price DESC, brand_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q53.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q53.sql
new file mode 100644
index 0000000000..e360b37cf4
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q53.sql
@@ -0,0 +1,27 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     i_manufact_id
+   , sum(ss_sales_price) sum_sales
+   , avg(sum(ss_sales_price)) OVER (PARTITION BY i_manufact_id) avg_quarterly_sales
+   FROM
+     item
+   , store_sales
+   , date_dim
+   , store
+   WHERE (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (d_month_seq IN (1200   , (1200 + 1)   , (1200 + 2)   , (1200 + 3)   , (1200 + 4)   , (1200 + 5)   , (1200 + 6)   , (1200 + 7)   , (1200 + 8)   , (1200 + 9)   , (1200 + 10)   , (1200 + 11)))
+      AND (((i_category IN ('Books'         , 'Children'         , 'Electronics'))
+            AND (i_class IN ('personal'         , 'portable'         , 'reference'         , 'self-help'))
+            AND (i_brand IN ('scholaramalgamalg #14'         , 'scholaramalgamalg #7'         , 'exportiunivamalg #9'         , 'scholaramalgamalg #9')))
+         OR ((i_category IN ('Women'         , 'Music'         , 'Men'))
+            AND (i_class IN ('accessories'         , 'classical'         , 'fragrances'         , 'pants'))
+            AND (i_brand IN ('amalgimporto #1'         , 'edu packscholar #1'         , 'exportiimporto #1'         , 'importoamalg #1'))))
+   GROUP BY i_manufact_id, d_qoy
+)  tmp1
+WHERE ((CASE WHEN (avg_quarterly_sales > 0) THEN (abs((sum_sales - avg_quarterly_sales)) / avg_quarterly_sales) ELSE null END) > 0.1)
+ORDER BY avg_quarterly_sales ASC, sum_sales ASC, i_manufact_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q54.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q54.sql
new file mode 100644
index 0000000000..b4f5a8d45e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q54.sql
@@ -0,0 +1,75 @@
+WITH
+  my_customers AS (
+   SELECT DISTINCT
+     c_customer_sk
+   , c_current_addr_sk
+   FROM
+     (
+      SELECT
+        cs_sold_date_sk sold_date_sk
+      , cs_bill_customer_sk customer_sk
+      , cs_item_sk item_sk
+      FROM
+        catalog_sales
+UNION ALL       SELECT
+        ws_sold_date_sk sold_date_sk
+      , ws_bill_customer_sk customer_sk
+      , ws_item_sk item_sk
+      FROM
+        web_sales
+   )  cs_or_ws_sales
+   , item
+   , date_dim
+   , customer
+   WHERE (sold_date_sk = d_date_sk)
+      AND (item_sk = i_item_sk)
+      AND (i_category = 'Women')
+      AND (i_class = 'maternity')
+      AND (c_customer_sk = cs_or_ws_sales.customer_sk)
+      AND (d_moy = 12)
+      AND (d_year = 1998)
+)
+, my_revenue AS (
+   SELECT
+     c_customer_sk
+   , sum(ss_ext_sales_price) revenue
+   FROM
+     my_customers
+   , store_sales
+   , customer_address
+   , store
+   , date_dim
+   WHERE (c_current_addr_sk = ca_address_sk)
+      AND (ca_county = s_county)
+      AND (ca_state = s_state)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (c_customer_sk = ss_customer_sk)
+      AND (d_month_seq BETWEEN (
+      SELECT DISTINCT (d_month_seq + 1)
+      FROM
+        date_dim
+      WHERE (d_year = 1998)
+         AND (d_moy = 12)
+   ) AND (
+      SELECT DISTINCT (d_month_seq + 3)
+      FROM
+        date_dim
+      WHERE (d_year = 1998)
+         AND (d_moy = 12)
+   ))
+   GROUP BY c_customer_sk
+)
+, segments AS (
+   SELECT CAST((revenue / 50) AS INTEGER) segment
+   FROM
+     my_revenue
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  segment
+, count(*) num_customers
+, (segment * 50) segment_base
+FROM
+  segments
+GROUP BY segment
+ORDER BY segment ASC, num_customers ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q55.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q55.sql
new file mode 100644
index 0000000000..238aed4fc4
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q55.sql
@@ -0,0 +1,16 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_brand_id brand_id
+, i_brand brand
+, sum(ss_ext_sales_price) ext_price
+FROM
+  date_dim
+, store_sales
+, item
+WHERE (d_date_sk = ss_sold_date_sk)
+   AND (ss_item_sk = i_item_sk)
+   AND (i_manager_id = 28)
+   AND (d_moy = 11)
+   AND (d_year = 1999)
+GROUP BY i_brand, i_brand_id
+ORDER BY ext_price DESC, i_brand_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q56.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q56.sql
new file mode 100644
index 0000000000..b89c8011fd
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q56.sql
@@ -0,0 +1,88 @@
+WITH
+  ss AS (
+   SELECT
+     i_item_id
+   , sum(ss_ext_sales_price) total_sales
+   FROM
+     store_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_color IN ('slate'      , 'blanched'      , 'burnished'))
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy = 2)
+      AND (ss_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+, cs AS (
+   SELECT
+     i_item_id
+   , sum(cs_ext_sales_price) total_sales
+   FROM
+     catalog_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_color IN ('slate'      , 'blanched'      , 'burnished'))
+   ))
+      AND (cs_item_sk = i_item_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy = 2)
+      AND (cs_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+, ws AS (
+   SELECT
+     i_item_id
+   , sum(ws_ext_sales_price) total_sales
+   FROM
+     web_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_color IN ('slate'      , 'blanched'      , 'burnished'))
+   ))
+      AND (ws_item_sk = i_item_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy = 2)
+      AND (ws_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, sum(total_sales) total_sales
+FROM
+  (
+   SELECT *
+   FROM
+     ss
+UNION ALL    SELECT *
+   FROM
+     cs
+UNION ALL    SELECT *
+   FROM
+     ws
+)  tmp1
+GROUP BY i_item_id
+ORDER BY total_sales ASC, i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q57.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q57.sql
new file mode 100644
index 0000000000..26e8007e47
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q57.sql
@@ -0,0 +1,58 @@
+WITH
+  v1 AS (
+   SELECT
+     i_category
+   , i_brand
+   , cc_name
+   , d_year
+   , d_moy
+   , sum(cs_sales_price) sum_sales
+   , avg(sum(cs_sales_price)) OVER (PARTITION BY i_category, i_brand, cc_name, d_year) avg_monthly_sales
+   , rank() OVER (PARTITION BY i_category, i_brand, cc_name ORDER BY d_year ASC, d_moy ASC) rn
+   FROM
+     item
+   , catalog_sales
+   , date_dim
+   , call_center
+   WHERE (cs_item_sk = i_item_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (cc_call_center_sk = cs_call_center_sk)
+      AND ((d_year = 1999)
+         OR ((d_year = (1999 - 1))
+            AND (d_moy = 12))
+         OR ((d_year = (1999 + 1))
+            AND (d_moy = 1)))
+   GROUP BY i_category, i_brand, cc_name, d_year, d_moy
+)
+, v2 AS (
+   SELECT
+     v1.i_category
+   , v1.i_brand
+   , v1.cc_name
+   , v1.d_year
+   , v1.d_moy
+   , v1.avg_monthly_sales
+   , v1.sum_sales
+   , v1_lag.sum_sales psum
+   , v1_lead.sum_sales nsum
+   FROM
+     v1
+   , v1 v1_lag
+   , v1 v1_lead
+   WHERE (v1.i_category = v1_lag.i_category)
+      AND (v1.i_category = v1_lead.i_category)
+      AND (v1.i_brand = v1_lag.i_brand)
+      AND (v1.i_brand = v1_lead.i_brand)
+      AND (v1.cc_name = v1_lag.cc_name)
+      AND (v1.cc_name = v1_lead.cc_name)
+      AND (v1.rn = (v1_lag.rn + 1))
+      AND (v1.rn = (v1_lead.rn - 1))
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  v2
+WHERE (d_year = 1999)
+   AND (avg_monthly_sales > 0)
+   AND ((CASE WHEN (avg_monthly_sales > 0) THEN (abs((sum_sales - avg_monthly_sales)) / avg_monthly_sales) ELSE null END) > 0.1)
+ORDER BY (sum_sales - avg_monthly_sales) ASC, 3 ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q58.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q58.sql
new file mode 100644
index 0000000000..bb23a44945
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q58.sql
@@ -0,0 +1,93 @@
+WITH
+  ss_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(ss_ext_sales_price) ss_item_rev
+   FROM
+     store_sales
+   , item
+   , date_dim
+   WHERE (ss_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq = (
+            SELECT d_week_seq
+            FROM
+              date_dim
+            WHERE (d_date = CAST('2000-01-03' AS DATE))
+         ))
+   ))
+      AND (ss_sold_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+, cs_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(cs_ext_sales_price) cs_item_rev
+   FROM
+     catalog_sales
+   , item
+   , date_dim
+   WHERE (cs_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq = (
+            SELECT d_week_seq
+            FROM
+              date_dim
+            WHERE (d_date = CAST('2000-01-03' AS DATE))
+         ))
+   ))
+      AND (cs_sold_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+, ws_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(ws_ext_sales_price) ws_item_rev
+   FROM
+     web_sales
+   , item
+   , date_dim
+   WHERE (ws_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq = (
+            SELECT d_week_seq
+            FROM
+              date_dim
+            WHERE (d_date = CAST('2000-01-03' AS DATE))
+         ))
+   ))
+      AND (ws_sold_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  ss_items.item_id
+, ss_item_rev
+, ((ss_item_rev / ((ss_item_rev + cs_item_rev) + ws_item_rev)) / 3) * 100 ss_dev
+, cs_item_rev
+, ((cs_item_rev / ((ss_item_rev + cs_item_rev) + ws_item_rev)) / 3) * 100 cs_dev
+, ws_item_rev
+, ((ws_item_rev / ((ss_item_rev + cs_item_rev) + ws_item_rev)) / 3) * 100 ws_dev
+, (((ss_item_rev + cs_item_rev) + ws_item_rev) / 3) average
+FROM
+  ss_items
+, cs_items
+, ws_items
+WHERE (ss_items.item_id = cs_items.item_id)
+   AND (ss_items.item_id = ws_items.item_id)
+   AND (ss_item_rev BETWEEN 0.9 * cs_item_rev AND 1.1 * cs_item_rev)
+   AND (ss_item_rev BETWEEN 0.9 * ws_item_rev AND 1.1 * ws_item_rev)
+   AND (cs_item_rev BETWEEN 0.9 * ss_item_rev AND 1.1 * ss_item_rev)
+   AND (cs_item_rev BETWEEN 0.9 * ws_item_rev AND 1.1 * ws_item_rev)
+   AND (ws_item_rev BETWEEN 0.9 * ss_item_rev AND 1.1 * ss_item_rev)
+   AND (ws_item_rev BETWEEN 0.9 * cs_item_rev AND 1.1 * cs_item_rev)
+ORDER BY ss_items.item_id ASC, ss_item_rev ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q59.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q59.sql
new file mode 100644
index 0000000000..63488fe931
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q59.sql
@@ -0,0 +1,74 @@
+WITH
+  wss AS (
+   SELECT
+     d_week_seq
+   , ss_store_sk
+   , sum((CASE WHEN (d_day_name = 'Sunday') THEN ss_sales_price ELSE null END)) sun_sales
+   , sum((CASE WHEN (d_day_name = 'Monday') THEN ss_sales_price ELSE null END)) mon_sales
+   , sum((CASE WHEN (d_day_name = 'Tuesday') THEN ss_sales_price ELSE null END)) tue_sales
+   , sum((CASE WHEN (d_day_name = 'Wednesday') THEN ss_sales_price ELSE null END)) wed_sales
+   , sum((CASE WHEN (d_day_name = 'Thursday ') THEN ss_sales_price ELSE null END)) thu_sales
+   , sum((CASE WHEN (d_day_name = 'Friday') THEN ss_sales_price ELSE null END)) fri_sales
+   , sum((CASE WHEN (d_day_name = 'Saturday') THEN ss_sales_price ELSE null END)) sat_sales
+   FROM
+     store_sales
+   , date_dim
+   WHERE (d_date_sk = ss_sold_date_sk)
+   GROUP BY d_week_seq, ss_store_sk
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  s_store_name1
+, s_store_id1
+, d_week_seq1
+, (sun_sales1 / sun_sales2)
+, (mon_sales1 / mon_sales2)
+, (tue_sales1 / tue_sales2)
+, (wed_sales1 / wed_sales2)
+, (thu_sales1 / thu_sales2)
+, (fri_sales1 / fri_sales2)
+, (sat_sales1 / sat_sales2)
+FROM
+  (
+   SELECT
+     s_store_name s_store_name1
+   , wss.d_week_seq d_week_seq1
+   , s_store_id s_store_id1
+   , sun_sales sun_sales1
+   , mon_sales mon_sales1
+   , tue_sales tue_sales1
+   , wed_sales wed_sales1
+   , thu_sales thu_sales1
+   , fri_sales fri_sales1
+   , sat_sales sat_sales1
+   FROM
+     wss
+   , store
+   , date_dim d
+   WHERE (d.d_week_seq = wss.d_week_seq)
+      AND (ss_store_sk = s_store_sk)
+      AND (d_month_seq BETWEEN 1212 AND (1212 + 11))
+)  y
+, (
+   SELECT
+     s_store_name s_store_name2
+   , wss.d_week_seq d_week_seq2
+   , s_store_id s_store_id2
+   , sun_sales sun_sales2
+   , mon_sales mon_sales2
+   , tue_sales tue_sales2
+   , wed_sales wed_sales2
+   , thu_sales thu_sales2
+   , fri_sales fri_sales2
+   , sat_sales sat_sales2
+   FROM
+     wss
+   , store
+   , date_dim d
+   WHERE (d.d_week_seq = wss.d_week_seq)
+      AND (ss_store_sk = s_store_sk)
+      AND (d_month_seq BETWEEN (1212 + 12) AND (1212 + 23))
+)  x
+WHERE (s_store_id1 = s_store_id2)
+   AND (d_week_seq1 = (d_week_seq2 - 52))
+ORDER BY s_store_name1 ASC, s_store_id1 ASC, d_week_seq1 ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q60.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q60.sql
new file mode 100644
index 0000000000..ace81acd7b
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q60.sql
@@ -0,0 +1,88 @@
+WITH
+  ss AS (
+   SELECT
+     i_item_id
+   , sum(ss_ext_sales_price) total_sales
+   FROM
+     store_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_category IN ('Music'))
+   ))
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 9)
+      AND (ss_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+, cs AS (
+   SELECT
+     i_item_id
+   , sum(cs_ext_sales_price) total_sales
+   FROM
+     catalog_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_category IN ('Music'))
+   ))
+      AND (cs_item_sk = i_item_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 9)
+      AND (cs_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+, ws AS (
+   SELECT
+     i_item_id
+   , sum(ws_ext_sales_price) total_sales
+   FROM
+     web_sales
+   , date_dim
+   , customer_address
+   , item
+   WHERE (i_item_id IN (
+      SELECT i_item_id
+      FROM
+        item
+      WHERE (i_category IN ('Music'))
+   ))
+      AND (ws_item_sk = i_item_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year = 1998)
+      AND (d_moy = 9)
+      AND (ws_bill_addr_sk = ca_address_sk)
+      AND (ca_gmt_offset = -5)
+   GROUP BY i_item_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, sum(total_sales) total_sales
+FROM
+  (
+   SELECT *
+   FROM
+     ss
+UNION ALL    SELECT *
+   FROM
+     cs
+UNION ALL    SELECT *
+   FROM
+     ws
+)  tmp1
+GROUP BY i_item_id
+ORDER BY i_item_id ASC, total_sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q61.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q61.sql
new file mode 100644
index 0000000000..ebac453195
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q61.sql
@@ -0,0 +1,52 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  promotions
+, total
+, ((CAST(promotions AS DECIMALV3(15,4)) / CAST(total AS DECIMALV3(15,4))) * 100)
+FROM
+  (
+   SELECT sum(ss_ext_sales_price) promotions
+   FROM
+     store_sales
+   , store
+   , promotion
+   , date_dim
+   , customer
+   , customer_address
+   , item
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (ss_promo_sk = p_promo_sk)
+      AND (ss_customer_sk = c_customer_sk)
+      AND (ca_address_sk = c_current_addr_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (ca_gmt_offset = -5)
+      AND (i_category = 'Jewelry')
+      AND ((p_channel_dmail = 'Y')
+         OR (p_channel_email = 'Y')
+         OR (p_channel_tv = 'Y'))
+      AND (s_gmt_offset = -5)
+      AND (d_year = 1998)
+      AND (d_moy = 11)
+)  promotional_sales
+, (
+   SELECT sum(ss_ext_sales_price) total
+   FROM
+     store_sales
+   , store
+   , date_dim
+   , customer
+   , customer_address
+   , item
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (ss_customer_sk = c_customer_sk)
+      AND (ca_address_sk = c_current_addr_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (ca_gmt_offset = -5)
+      AND (i_category = 'Jewelry')
+      AND (s_gmt_offset = -5)
+      AND (d_year = 1998)
+      AND (d_moy = 11)
+)  all_sales
+ORDER BY promotions ASC, total ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q62.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q62.sql
new file mode 100644
index 0000000000..5d8d7550a6
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q62.sql
@@ -0,0 +1,26 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  substr(w_warehouse_name, 1, 20)
+, sm_type
+, web_name
+, sum((CASE WHEN ((ws_ship_date_sk - ws_sold_date_sk) <= 30) THEN 1 ELSE 0 END)) '30 days'
+, sum((CASE WHEN ((ws_ship_date_sk - ws_sold_date_sk) > 30)
+   AND ((ws_ship_date_sk - ws_sold_date_sk) <= 60) THEN 1 ELSE 0 END)) '31-60 days'
+, sum((CASE WHEN ((ws_ship_date_sk - ws_sold_date_sk) > 60)
+   AND ((ws_ship_date_sk - ws_sold_date_sk) <= 90) THEN 1 ELSE 0 END)) '61-90 days'
+, sum((CASE WHEN ((ws_ship_date_sk - ws_sold_date_sk) > 90)
+   AND ((ws_ship_date_sk - ws_sold_date_sk) <= 120) THEN 1 ELSE 0 END)) '91-120 days'
+, sum((CASE WHEN ((ws_ship_date_sk - ws_sold_date_sk) > 120) THEN 1 ELSE 0 END)) '>120 days'
+FROM
+  web_sales
+, warehouse
+, ship_mode
+, web_site
+, date_dim
+WHERE (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   AND (ws_ship_date_sk = d_date_sk)
+   AND (ws_warehouse_sk = w_warehouse_sk)
+   AND (ws_ship_mode_sk = sm_ship_mode_sk)
+   AND (ws_web_site_sk = web_site_sk)
+GROUP BY substr(w_warehouse_name, 1, 20), sm_type, web_name
+ORDER BY substr(w_warehouse_name, 1, 20) ASC, sm_type ASC, web_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q63.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q63.sql
new file mode 100644
index 0000000000..36252bde89
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q63.sql
@@ -0,0 +1,27 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     i_manager_id
+   , sum(ss_sales_price) sum_sales
+   , avg(sum(ss_sales_price)) OVER (PARTITION BY i_manager_id) avg_monthly_sales
+   FROM
+     item
+   , store_sales
+   , date_dim
+   , store
+   WHERE (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (d_month_seq IN (1200   , (1200 + 1)   , (1200 + 2)   , (1200 + 3)   , (1200 + 4)   , (1200 + 5)   , (1200 + 6)   , (1200 + 7)   , (1200 + 8)   , (1200 + 9)   , (1200 + 10)   , (1200 + 11)))
+      AND (((i_category IN ('Books'         , 'Children'         , 'Electronics'))
+            AND (i_class IN ('personal'         , 'portable'         , 'refernece'         , 'self-help'))
+            AND (i_brand IN ('scholaramalgamalg #14'         , 'scholaramalgamalg #7'         , 'exportiunivamalg #9'         , 'scholaramalgamalg #9')))
+         OR ((i_category IN ('Women'         , 'Music'         , 'Men'))
+            AND (i_class IN ('accessories'         , 'classical'         , 'fragrances'         , 'pants'))
+            AND (i_brand IN ('amalgimporto #1'         , 'edu packscholar #1'         , 'exportiimporto #1'         , 'importoamalg #1'))))
+   GROUP BY i_manager_id, d_moy
+)  tmp1
+WHERE ((CASE WHEN (avg_monthly_sales > 0) THEN (abs((sum_sales - avg_monthly_sales)) / avg_monthly_sales) ELSE null END) > 0.1)
+ORDER BY i_manager_id ASC, avg_monthly_sales ASC, sum_sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q64.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q64.sql
new file mode 100644
index 0000000000..3eeda87f6f
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q64.sql
@@ -0,0 +1,110 @@
+WITH
+  cs_ui AS (
+   SELECT
+     cs_item_sk
+   , sum(cs_ext_list_price) sale
+   , sum(((cr_refunded_cash + cr_reversed_charge) + cr_store_credit)) refund
+   FROM
+     catalog_sales
+   , catalog_returns
+   WHERE (cs_item_sk = cr_item_sk)
+      AND (cs_order_number = cr_order_number)
+   GROUP BY cs_item_sk
+   HAVING (sum(cs_ext_list_price) > (2 * sum(((cr_refunded_cash + cr_reversed_charge) + cr_store_credit))))
+)
+, cross_sales AS (
+   SELECT
+     i_product_name product_name
+   , i_item_sk item_sk
+   , s_store_name store_name
+   , s_zip store_zip
+   , ad1.ca_street_number b_street_number
+   , ad1.ca_street_name b_street_name
+   , ad1.ca_city b_city
+   , ad1.ca_zip b_zip
+   , ad2.ca_street_number c_street_number
+   , ad2.ca_street_name c_street_name
+   , ad2.ca_city c_city
+   , ad2.ca_zip c_zip
+   , d1.d_year syear
+   , d2.d_year fsyear
+   , d3.d_year s2year
+   , count(*) cnt
+   , sum(ss_wholesale_cost) s1
+   , sum(ss_list_price) s2
+   , sum(ss_coupon_amt) s3
+   FROM
+     store_sales
+   , store_returns
+   , cs_ui
+   , date_dim d1
+   , date_dim d2
+   , date_dim d3
+   , store
+   , customer
+   , customer_demographics cd1
+   , customer_demographics cd2
+   , promotion
+   , household_demographics hd1
+   , household_demographics hd2
+   , customer_address ad1
+   , customer_address ad2
+   , income_band ib1
+   , income_band ib2
+   , item
+   WHERE (ss_store_sk = s_store_sk)
+      AND (ss_sold_date_sk = d1.d_date_sk)
+      AND (ss_customer_sk = c_customer_sk)
+      AND (ss_cdemo_sk = cd1.cd_demo_sk)
+      AND (ss_hdemo_sk = hd1.hd_demo_sk)
+      AND (ss_addr_sk = ad1.ca_address_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (ss_item_sk = sr_item_sk)
+      AND (ss_ticket_number = sr_ticket_number)
+      AND (ss_item_sk = cs_ui.cs_item_sk)
+      AND (c_current_cdemo_sk = cd2.cd_demo_sk)
+      AND (c_current_hdemo_sk = hd2.hd_demo_sk)
+      AND (c_current_addr_sk = ad2.ca_address_sk)
+      AND (c_first_sales_date_sk = d2.d_date_sk)
+      AND (c_first_shipto_date_sk = d3.d_date_sk)
+      AND (ss_promo_sk = p_promo_sk)
+      AND (hd1.hd_income_band_sk = ib1.ib_income_band_sk)
+      AND (hd2.hd_income_band_sk = ib2.ib_income_band_sk)
+      AND (cd1.cd_marital_status <> cd2.cd_marital_status)
+      AND (i_color IN ('purple'   , 'burlywood'   , 'indian'   , 'spring'   , 'floral'   , 'medium'))
+      AND (i_current_price BETWEEN 64 AND (64 + 10))
+      AND (i_current_price BETWEEN (64 + 1) AND (64 + 15))
+   GROUP BY i_product_name, i_item_sk, s_store_name, s_zip, ad1.ca_street_number, ad1.ca_street_name, ad1.ca_city, ad1.ca_zip, ad2.ca_street_number, ad2.ca_street_name, ad2.ca_city, ad2.ca_zip, d1.d_year, d2.d_year, d3.d_year
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  cs1.product_name
+, cs1.store_name
+, cs1.store_zip
+, cs1.b_street_number
+, cs1.b_street_name
+, cs1.b_city
+, cs1.b_zip
+, cs1.c_street_number
+, cs1.c_street_name
+, cs1.c_city
+, cs1.c_zip
+, cs1.syear
+, cs1.cnt
+, cs1.s1 s11
+, cs1.s2 s21
+, cs1.s3 s31
+, cs2.s1 s12
+, cs2.s2 s22
+, cs2.s3 s32
+, cs2.syear
+, cs2.cnt
+FROM
+  cross_sales cs1
+, cross_sales cs2
+WHERE (cs1.item_sk = cs2.item_sk)
+   AND (cs1.syear = 1999)
+   AND (cs2.syear = (1999 + 1))
+   AND (cs2.cnt <= cs1.cnt)
+   AND (cs1.store_name = cs2.store_name)
+   AND (cs1.store_zip = cs2.store_zip)
+ORDER BY cs1.product_name ASC, cs1.store_name ASC, cs2.cnt ASC, 14, 15, 16, 17, 18
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q65.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q65.sql
new file mode 100644
index 0000000000..4f74973fb0
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q65.sql
@@ -0,0 +1,47 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  s_store_name
+, i_item_desc
+, sc.revenue
+, i_current_price
+, i_wholesale_cost
+, i_brand
+FROM
+  store
+, item
+, (
+   SELECT
+     ss_store_sk
+   , avg(revenue) ave
+   FROM
+     (
+      SELECT
+        ss_store_sk
+      , ss_item_sk
+      , sum(ss_sales_price) revenue
+      FROM
+        store_sales
+      , date_dim
+      WHERE (ss_sold_date_sk = d_date_sk)
+         AND (d_month_seq BETWEEN 1176 AND (1176 + 11))
+      GROUP BY ss_store_sk, ss_item_sk
+   )  sa
+   GROUP BY ss_store_sk
+)  sb
+, (
+   SELECT
+     ss_store_sk
+   , ss_item_sk
+   , sum(ss_sales_price) revenue
+   FROM
+     store_sales
+   , date_dim
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (d_month_seq BETWEEN 1176 AND (1176 + 11))
+   GROUP BY ss_store_sk, ss_item_sk
+)  sc
+WHERE (sb.ss_store_sk = sc.ss_store_sk)
+   AND (sc.revenue <= (0.1 * sb.ave))
+   AND (s_store_sk = sc.ss_store_sk)
+   AND (i_item_sk = sc.ss_item_sk)
+ORDER BY s_store_name ASC, i_item_desc ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q66.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q66.sql
new file mode 100644
index 0000000000..f113fb64dd
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q66.sql
@@ -0,0 +1,146 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  w_warehouse_name
+, w_warehouse_sq_ft
+, w_city
+, w_county
+, w_state
+, w_country
+, ship_carriers
+, year
+, sum(jan_sales) jan_sales
+, sum(feb_sales) feb_sales
+, sum(mar_sales) mar_sales
+, sum(apr_sales) apr_sales
+, sum(may_sales) may_sales
+, sum(jun_sales) jun_sales
+, sum(jul_sales) jul_sales
+, sum(aug_sales) aug_sales
+, sum(sep_sales) sep_sales
+, sum(oct_sales) oct_sales
+, sum(nov_sales) nov_sales
+, sum(dec_sales) dec_sales
+, sum((jan_sales / w_warehouse_sq_ft)) jan_sales_per_sq_foot
+, sum((feb_sales / w_warehouse_sq_ft)) feb_sales_per_sq_foot
+, sum((mar_sales / w_warehouse_sq_ft)) mar_sales_per_sq_foot
+, sum((apr_sales / w_warehouse_sq_ft)) apr_sales_per_sq_foot
+, sum((may_sales / w_warehouse_sq_ft)) may_sales_per_sq_foot
+, sum((jun_sales / w_warehouse_sq_ft)) jun_sales_per_sq_foot
+, sum((jul_sales / w_warehouse_sq_ft)) jul_sales_per_sq_foot
+, sum((aug_sales / w_warehouse_sq_ft)) aug_sales_per_sq_foot
+, sum((sep_sales / w_warehouse_sq_ft)) sep_sales_per_sq_foot
+, sum((oct_sales / w_warehouse_sq_ft)) oct_sales_per_sq_foot
+, sum((nov_sales / w_warehouse_sq_ft)) nov_sales_per_sq_foot
+, sum((dec_sales / w_warehouse_sq_ft)) dec_sales_per_sq_foot
+, sum(jan_net) jan_net
+, sum(feb_net) feb_net
+, sum(mar_net) mar_net
+, sum(apr_net) apr_net
+, sum(may_net) may_net
+, sum(jun_net) jun_net
+, sum(jul_net) jul_net
+, sum(aug_net) aug_net
+, sum(sep_net) sep_net
+, sum(oct_net) oct_net
+, sum(nov_net) nov_net
+, sum(dec_net) dec_net
+FROM
+(
+      SELECT
+        w_warehouse_name
+      , w_warehouse_sq_ft
+      , w_city
+      , w_county
+      , w_state
+      , w_country
+      , concat(concat('DHL', ','), 'BARIAN') ship_carriers
+      , d_year YEAR
+      , sum((CASE WHEN (d_moy = 1) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jan_sales
+      , sum((CASE WHEN (d_moy = 2) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) feb_sales
+      , sum((CASE WHEN (d_moy = 3) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) mar_sales
+      , sum((CASE WHEN (d_moy = 4) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) apr_sales
+      , sum((CASE WHEN (d_moy = 5) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) may_sales
+      , sum((CASE WHEN (d_moy = 6) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jun_sales
+      , sum((CASE WHEN (d_moy = 7) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jul_sales
+      , sum((CASE WHEN (d_moy = 8) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) aug_sales
+      , sum((CASE WHEN (d_moy = 9) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) sep_sales
+      , sum((CASE WHEN (d_moy = 10) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) oct_sales
+      , sum((CASE WHEN (d_moy = 11) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) nov_sales
+      , sum((CASE WHEN (d_moy = 12) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) dec_sales
+      , sum((CASE WHEN (d_moy = 1) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jan_net
+      , sum((CASE WHEN (d_moy = 2) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) feb_net
+      , sum((CASE WHEN (d_moy = 3) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) mar_net
+      , sum((CASE WHEN (d_moy = 4) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) apr_net
+      , sum((CASE WHEN (d_moy = 5) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) may_net
+      , sum((CASE WHEN (d_moy = 6) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jun_net
+      , sum((CASE WHEN (d_moy = 7) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jul_net
+      , sum((CASE WHEN (d_moy = 8) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) aug_net
+      , sum((CASE WHEN (d_moy = 9) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) sep_net
+      , sum((CASE WHEN (d_moy = 10) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) oct_net
+      , sum((CASE WHEN (d_moy = 11) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) nov_net
+      , sum((CASE WHEN (d_moy = 12) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) dec_net
+      FROM
+        web_sales
+      , warehouse
+      , date_dim
+      , time_dim
+      , ship_mode
+      WHERE (ws_warehouse_sk = w_warehouse_sk)
+         AND (ws_sold_date_sk = d_date_sk)
+         AND (ws_sold_time_sk = t_time_sk)
+         AND (ws_ship_mode_sk = sm_ship_mode_sk)
+         AND (d_year = 2001)
+         AND (t_time BETWEEN 30838 AND (30838 + 28800))
+         AND (sm_carrier IN ('DHL'      , 'BARIAN'))
+      GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, d_year
+   UNION ALL
+      SELECT
+        w_warehouse_name
+      , w_warehouse_sq_ft
+      , w_city
+      , w_county
+      , w_state
+      , w_country
+      , concat(concat('DHL', ','), 'BARIAN') ship_carriers
+      , d_year YEAR
+      , sum((CASE WHEN (d_moy = 1) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jan_sales
+      , sum((CASE WHEN (d_moy = 2) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) feb_sales
+      , sum((CASE WHEN (d_moy = 3) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) mar_sales
+      , sum((CASE WHEN (d_moy = 4) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) apr_sales
+      , sum((CASE WHEN (d_moy = 5) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) may_sales
+      , sum((CASE WHEN (d_moy = 6) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jun_sales
+      , sum((CASE WHEN (d_moy = 7) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jul_sales
+      , sum((CASE WHEN (d_moy = 8) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) aug_sales
+      , sum((CASE WHEN (d_moy = 9) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) sep_sales
+      , sum((CASE WHEN (d_moy = 10) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) oct_sales
+      , sum((CASE WHEN (d_moy = 11) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) nov_sales
+      , sum((CASE WHEN (d_moy = 12) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) dec_sales
+      , sum((CASE WHEN (d_moy = 1) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jan_net
+      , sum((CASE WHEN (d_moy = 2) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) feb_net
+      , sum((CASE WHEN (d_moy = 3) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) mar_net
+      , sum((CASE WHEN (d_moy = 4) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) apr_net
+      , sum((CASE WHEN (d_moy = 5) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) may_net
+      , sum((CASE WHEN (d_moy = 6) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jun_net
+      , sum((CASE WHEN (d_moy = 7) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jul_net
+      , sum((CASE WHEN (d_moy = 8) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) aug_net
+      , sum((CASE WHEN (d_moy = 9) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) sep_net
+      , sum((CASE WHEN (d_moy = 10) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) oct_net
+      , sum((CASE WHEN (d_moy = 11) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) nov_net
+      , sum((CASE WHEN (d_moy = 12) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) dec_net
+      FROM
+        catalog_sales
+      , warehouse
+      , date_dim
+      , time_dim
+      , ship_mode
+      WHERE (cs_warehouse_sk = w_warehouse_sk)
+         AND (cs_sold_date_sk = d_date_sk)
+         AND (cs_sold_time_sk = t_time_sk)
+         AND (cs_ship_mode_sk = sm_ship_mode_sk)
+         AND (d_year = 2001)
+         AND (t_time BETWEEN 30838 AND (30838 + 28800))
+         AND (sm_carrier IN ('DHL'      , 'BARIAN'))
+      GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, d_year
+   )  x
+GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, ship_carriers, year
+ORDER BY w_warehouse_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q67.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q67.sql
new file mode 100644
index 0000000000..b832c8e372
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q67.sql
@@ -0,0 +1,41 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     i_category
+   , i_class
+   , i_brand
+   , i_product_name
+   , d_year
+   , d_qoy
+   , d_moy
+   , s_store_id
+   , sumsales
+   , rank() OVER (PARTITION BY i_category ORDER BY sumsales DESC) rk
+   FROM
+     (
+      SELECT
+        i_category
+      , i_class
+      , i_brand
+      , i_product_name
+      , d_year
+      , d_qoy
+      , d_moy
+      , s_store_id
+      , sum(COALESCE((ss_sales_price * ss_quantity), 0)) sumsales
+      FROM
+        store_sales
+      , date_dim
+      , store
+      , item
+      WHERE (ss_sold_date_sk = d_date_sk)
+         AND (ss_item_sk = i_item_sk)
+         AND (ss_store_sk = s_store_sk)
+         AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+      GROUP BY ROLLUP (i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy, s_store_id)
+   )  dw1
+)  dw2
+WHERE (rk <= 100)
+ORDER BY i_category ASC, i_class ASC, i_brand ASC, i_product_name ASC, d_year ASC, d_qoy ASC, d_moy ASC, s_store_id ASC, sumsales ASC, rk ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q68.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q68.sql
new file mode 100644
index 0000000000..528ae18cc7
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q68.sql
@@ -0,0 +1,42 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, ca_city
+, bought_city
+, ss_ticket_number
+, extended_price
+, extended_tax
+, list_price
+FROM
+  (
+   SELECT
+     ss_ticket_number
+   , ss_customer_sk
+   , ca_city bought_city
+   , sum(ss_ext_sales_price) extended_price
+   , sum(ss_ext_list_price) list_price
+   , sum(ss_ext_tax) extended_tax
+   FROM
+     store_sales
+   , date_dim
+   , store
+   , household_demographics
+   , customer_address
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_store_sk = store.s_store_sk)
+      AND (store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (store_sales.ss_addr_sk = customer_address.ca_address_sk)
+      AND (date_dim.d_dom BETWEEN 1 AND 2)
+      AND ((household_demographics.hd_dep_count = 4)
+         OR (household_demographics.hd_vehicle_count = 3))
+      AND (date_dim.d_year IN (1999   , (1999 + 1)   , (1999 + 2)))
+      AND (store.s_city IN ('Midway'   , 'Fairview'))
+   GROUP BY ss_ticket_number, ss_customer_sk, ss_addr_sk, ca_city
+)  dn
+, customer
+, customer_address current_addr
+WHERE (ss_customer_sk = c_customer_sk)
+   AND (customer.c_current_addr_sk = current_addr.ca_address_sk)
+   AND (current_addr.ca_city <> bought_city)
+ORDER BY c_last_name ASC, ss_ticket_number ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q69.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q69.sql
new file mode 100644
index 0000000000..77bf5ab66c
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q69.sql
@@ -0,0 +1,49 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  cd_gender
+, cd_marital_status
+, cd_education_status
+, count(*) cnt1
+, cd_purchase_estimate
+, count(*) cnt2
+, cd_credit_rating
+, count(*) cnt3
+FROM
+  customer c
+, customer_address ca
+, customer_demographics
+WHERE (c.c_current_addr_sk = ca.ca_address_sk)
+   AND (ca_state IN ('KY', 'GA', 'NM'))
+   AND (cd_demo_sk = c.c_current_cdemo_sk)
+   AND (EXISTS (
+   SELECT *
+   FROM
+     store_sales
+   , date_dim
+   WHERE (c.c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy BETWEEN 4 AND (4 + 2))
+))
+   AND (NOT (EXISTS (
+   SELECT *
+   FROM
+     web_sales
+   , date_dim
+   WHERE (c.c_customer_sk = ws_bill_customer_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy BETWEEN 4 AND (4 + 2))
+)))
+   AND (NOT (EXISTS (
+   SELECT *
+   FROM
+     catalog_sales
+   , date_dim
+   WHERE (c.c_customer_sk = cs_ship_customer_sk)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (d_year = 2001)
+      AND (d_moy BETWEEN 4 AND (4 + 2))
+)))
+GROUP BY cd_gender, cd_marital_status, cd_education_status, cd_purchase_estimate, cd_credit_rating
+ORDER BY cd_gender ASC, cd_marital_status ASC, cd_education_status ASC, cd_purchase_estimate ASC, cd_credit_rating ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q70.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q70.sql
new file mode 100644
index 0000000000..918c303929
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q70.sql
@@ -0,0 +1,34 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  sum(ss_net_profit) total_sum
+, s_state
+, s_county
+, (GROUPING (s_state) + GROUPING (s_county)) lochierarchy
+, rank() OVER (PARTITION BY (GROUPING (s_state) + GROUPING (s_county)), (CASE WHEN (GROUPING (s_county) = 0) THEN s_state END) ORDER BY sum(ss_net_profit) DESC) rank_within_parent
+FROM
+  store_sales
+, date_dim d1
+, store
+WHERE (d1.d_month_seq BETWEEN 1200 AND (1200 + 11))
+   AND (d1.d_date_sk = ss_sold_date_sk)
+   AND (s_store_sk = ss_store_sk)
+   AND (s_state IN (
+   SELECT s_state
+   FROM
+     (
+      SELECT
+        s_state s_state
+      , rank() OVER (PARTITION BY s_state ORDER BY sum(ss_net_profit) DESC) ranking
+      FROM
+        store_sales
+      , store
+      , date_dim
+      WHERE (d_month_seq BETWEEN 1200 AND (1200 + 11))
+         AND (d_date_sk = ss_sold_date_sk)
+         AND (s_store_sk = ss_store_sk)
+      GROUP BY s_state
+   )  tmp1
+   WHERE (ranking <= 5)
+))
+GROUP BY ROLLUP (s_state, s_county)
+ORDER BY lochierarchy DESC, (CASE WHEN (lochierarchy = 0) THEN s_state END) ASC, rank_within_parent ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q71.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q71.sql
new file mode 100644
index 0000000000..97b3abe9c1
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q71.sql
@@ -0,0 +1,51 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_brand_id brand_id
+, i_brand brand
+, t_hour
+, t_minute
+, sum(ext_price) ext_price
+FROM
+  item
+, (
+   SELECT
+     ws_ext_sales_price ext_price
+   , ws_sold_date_sk sold_date_sk
+   , ws_item_sk sold_item_sk
+   , ws_sold_time_sk time_sk
+   FROM
+     web_sales
+   , date_dim
+   WHERE (d_date_sk = ws_sold_date_sk)
+      AND (d_moy = 11)
+      AND (d_year = 1999)
+UNION ALL    SELECT
+     cs_ext_sales_price ext_price
+   , cs_sold_date_sk sold_date_sk
+   , cs_item_sk sold_item_sk
+   , cs_sold_time_sk time_sk
+   FROM
+     catalog_sales
+   , date_dim
+   WHERE (d_date_sk = cs_sold_date_sk)
+      AND (d_moy = 11)
+      AND (d_year = 1999)
+UNION ALL    SELECT
+     ss_ext_sales_price ext_price
+   , ss_sold_date_sk sold_date_sk
+   , ss_item_sk sold_item_sk
+   , ss_sold_time_sk time_sk
+   FROM
+     store_sales
+   , date_dim
+   WHERE (d_date_sk = ss_sold_date_sk)
+      AND (d_moy = 11)
+      AND (d_year = 1999)
+)  tmp
+, time_dim
+WHERE (sold_item_sk = i_item_sk)
+   AND (i_manager_id = 1)
+   AND (time_sk = t_time_sk)
+   AND ((t_meal_time = 'breakfast')
+      OR (t_meal_time = 'dinner'))
+GROUP BY i_brand, i_brand_id, t_hour, t_minute
+ORDER BY ext_price DESC, i_brand_id ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q72.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q72.sql
new file mode 100644
index 0000000000..8965a16a27
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q72.sql
@@ -0,0 +1,32 @@
+-- For ASAN build, it will take too long time to run q72, disable for now
+/*
+SELECT
+  i_item_desc
+, w_warehouse_name
+, d1.d_week_seq
+, sum((CASE WHEN (p_promo_sk IS NULL) THEN 1 ELSE 0 END)) no_promo
+, sum((CASE WHEN (p_promo_sk IS NOT NULL) THEN 1 ELSE 0 END)) promo
+, count(*) total_cnt
+FROM
+  catalog_sales
+INNER JOIN inventory ON (cs_item_sk = inv_item_sk)
+INNER JOIN warehouse ON (w_warehouse_sk = inv_warehouse_sk)
+INNER JOIN item ON (i_item_sk = cs_item_sk)
+INNER JOIN customer_demographics ON (cs_bill_cdemo_sk = cd_demo_sk)
+INNER JOIN household_demographics ON (cs_bill_hdemo_sk = hd_demo_sk)
+INNER JOIN date_dim d1 ON (cs_sold_date_sk = d1.d_date_sk)
+INNER JOIN date_dim d2 ON (inv_date_sk = d2.d_date_sk)
+INNER JOIN date_dim d3 ON (cs_ship_date_sk = d3.d_date_sk)
+LEFT JOIN promotion ON (cs_promo_sk = p_promo_sk)
+LEFT JOIN catalog_returns ON (cr_item_sk = cs_item_sk)
+   AND (cr_order_number = cs_order_number)
+WHERE (d1.d_week_seq = d2.d_week_seq)
+   AND (inv_quantity_on_hand < cs_quantity)
+   AND (d3.d_date > (d1.d_date + INTERVAL  '5' DAY))
+   AND (hd_buy_potential = '>10000')
+   AND (d1.d_year = 1999)
+   AND (cd_marital_status = 'D')
+GROUP BY i_item_desc, w_warehouse_name, d1.d_week_seq
+ORDER BY total_cnt DESC, i_item_desc ASC, w_warehouse_name ASC, d1.d_week_seq ASC
+LIMIT 100
+*/
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q73.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q73.sql
new file mode 100644
index 0000000000..4fe7f4bafd
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q73.sql
@@ -0,0 +1,34 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, c_salutation
+, c_preferred_cust_flag
+, ss_ticket_number
+, cnt
+FROM
+  (
+   SELECT
+     ss_ticket_number
+   , ss_customer_sk
+   , count(*) cnt
+   FROM
+     store_sales
+   , date_dim
+   , store
+   , household_demographics
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_store_sk = store.s_store_sk)
+      AND (store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (date_dim.d_dom BETWEEN 1 AND 2)
+      AND ((household_demographics.hd_buy_potential = '>10000')
+         OR (household_demographics.hd_buy_potential = 'Unknown'))
+      AND (household_demographics.hd_vehicle_count > 0)
+      AND ((CASE WHEN (household_demographics.hd_vehicle_count > 0) THEN (household_demographics.hd_dep_count / household_demographics.hd_vehicle_count) ELSE null END) > 1)
+      AND (date_dim.d_year IN (1999   , (1999 + 1)   , (1999 + 2)))
+      AND (store.s_county IN ('Williamson County'   , 'Franklin Parish'   , 'Bronx County'   , 'Orange County'))
+   GROUP BY ss_ticket_number, ss_customer_sk
+)  dj
+, customer
+WHERE (ss_customer_sk = c_customer_sk)
+   AND (cnt BETWEEN 1 AND 5)
+ORDER BY cnt DESC, c_last_name ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q74.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q74.sql
new file mode 100644
index 0000000000..00736ad196
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q74.sql
@@ -0,0 +1,58 @@
+WITH
+  year_total AS (
+   SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , d_year YEAR
+   , sum(ss_net_paid) year_total
+   , 's' sale_type
+   FROM
+     customer
+   , store_sales
+   , date_dim
+   WHERE (c_customer_sk = ss_customer_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (d_year IN (2001   , (2001 + 1)))
+   GROUP BY c_customer_id, c_first_name, c_last_name, d_year
+UNION ALL    SELECT
+     c_customer_id customer_id
+   , c_first_name customer_first_name
+   , c_last_name customer_last_name
+   , d_year YEAR
+   , sum(ws_net_paid) year_total
+   , 'w' sale_type
+   FROM
+     customer
+   , web_sales
+   , date_dim
+   WHERE (c_customer_sk = ws_bill_customer_sk)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (d_year IN (2001   , (2001 + 1)))
+   GROUP BY c_customer_id, c_first_name, c_last_name, d_year
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  t_s_secyear.customer_id
+, t_s_secyear.customer_first_name
+, t_s_secyear.customer_last_name
+FROM
+  year_total t_s_firstyear
+, year_total t_s_secyear
+, year_total t_w_firstyear
+, year_total t_w_secyear
+WHERE (t_s_secyear.customer_id = t_s_firstyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_secyear.customer_id)
+   AND (t_s_firstyear.customer_id = t_w_firstyear.customer_id)
+   AND (t_s_firstyear.sale_type = 's')
+   AND (t_w_firstyear.sale_type = 'w')
+   AND (t_s_secyear.sale_type = 's')
+   AND (t_w_secyear.sale_type = 'w')
+   AND (t_s_firstyear.year = 2001)
+   AND (t_s_secyear.year = (2001 + 1))
+   AND (t_w_firstyear.year = 2001)
+   AND (t_w_secyear.year = (2001 + 1))
+   AND (t_s_firstyear.year_total > 0)
+   AND (t_w_firstyear.year_total > 0)
+   AND ((CASE WHEN (t_w_firstyear.year_total > 0) THEN (t_w_secyear.year_total / t_w_firstyear.year_total) ELSE null END) > (CASE WHEN (t_s_firstyear.year_total > 0) THEN (t_s_secyear.year_total / t_s_firstyear.year_total) ELSE null END))
+ORDER BY 1 ASC, 1 ASC, 1 ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q75.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q75.sql
new file mode 100644
index 0000000000..2b96f1b2ef
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q75.sql
@@ -0,0 +1,83 @@
+WITH
+  all_sales AS (
+   SELECT
+     d_year
+   , i_brand_id
+   , i_class_id
+   , i_category_id
+   , i_manufact_id
+   , sum(sales_cnt) sales_cnt
+   , sum(sales_amt) sales_amt
+   FROM
+     (
+      SELECT
+        d_year
+      , i_brand_id
+      , i_class_id
+      , i_category_id
+      , i_manufact_id
+      , (cs_quantity - COALESCE(cr_return_quantity, 0)) sales_cnt
+      , (cs_ext_sales_price - COALESCE(cr_return_amount, 0.0)) sales_amt
+      FROM
+        catalog_sales
+      INNER JOIN item ON (i_item_sk = cs_item_sk)
+      INNER JOIN date_dim ON (d_date_sk = cs_sold_date_sk)
+      LEFT JOIN catalog_returns ON (cs_order_number = cr_order_number)
+         AND (cs_item_sk = cr_item_sk)
+      WHERE (i_category = 'Books')
+UNION       SELECT
+        d_year
+      , i_brand_id
+      , i_class_id
+      , i_category_id
+      , i_manufact_id
+      , (ss_quantity - COALESCE(sr_return_quantity, 0)) sales_cnt
+      , (ss_ext_sales_price - COALESCE(sr_return_amt, 0.0)) sales_amt
+      FROM
+        store_sales
+      INNER JOIN item ON (i_item_sk = ss_item_sk)
+      INNER JOIN date_dim ON (d_date_sk = ss_sold_date_sk)
+      LEFT JOIN store_returns ON (ss_ticket_number = sr_ticket_number)
+         AND (ss_item_sk = sr_item_sk)
+      WHERE (i_category = 'Books')
+UNION       SELECT
+        d_year
+      , i_brand_id
+      , i_class_id
+      , i_category_id
+      , i_manufact_id
+      , (ws_quantity - COALESCE(wr_return_quantity, 0)) sales_cnt
+      , (ws_ext_sales_price - COALESCE(wr_return_amt, 0.0)) sales_amt
+      FROM
+        web_sales
+      INNER JOIN item ON (i_item_sk = ws_item_sk)
+      INNER JOIN date_dim ON (d_date_sk = ws_sold_date_sk)
+      LEFT JOIN web_returns ON (ws_order_number = wr_order_number)
+         AND (ws_item_sk = wr_item_sk)
+      WHERE (i_category = 'Books')
+   )  sales_detail
+   GROUP BY d_year, i_brand_id, i_class_id, i_category_id, i_manufact_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  prev_yr.d_year prev_year
+, curr_yr.d_year year
+, curr_yr.i_brand_id
+, curr_yr.i_class_id
+, curr_yr.i_category_id
+, curr_yr.i_manufact_id
+, prev_yr.sales_cnt prev_yr_cnt
+, curr_yr.sales_cnt curr_yr_cnt
+, (curr_yr.sales_cnt - prev_yr.sales_cnt) sales_cnt_diff
+, (curr_yr.sales_amt - prev_yr.sales_amt) sales_amt_diff
+FROM
+  all_sales curr_yr
+, all_sales prev_yr
+WHERE (curr_yr.i_brand_id = prev_yr.i_brand_id)
+   AND (curr_yr.i_class_id = prev_yr.i_class_id)
+   AND (curr_yr.i_category_id = prev_yr.i_category_id)
+   AND (curr_yr.i_manufact_id = prev_yr.i_manufact_id)
+   AND (curr_yr.d_year = 2002)
+   AND (prev_yr.d_year = (2002 - 1))
+   AND ((CAST(curr_yr.sales_cnt AS DECIMALV3(17,2)) / CAST(prev_yr.sales_cnt AS DECIMALV3(17,2))) < 0.9)
+ORDER BY sales_cnt_diff ASC, sales_amt_diff ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q76.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q76.sql
new file mode 100644
index 0000000000..175fad693a
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q76.sql
@@ -0,0 +1,56 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  channel
+, col_name
+, d_year
+, d_qoy
+, i_category
+, count(*) sales_cnt
+, sum(ext_sales_price) sales_amt
+FROM
+  (
+   SELECT
+     'store' channel
+   , 'ss_store_sk' col_name
+   , d_year
+   , d_qoy
+   , i_category
+   , ss_ext_sales_price ext_sales_price
+   FROM
+     store_sales
+   , item
+   , date_dim
+   WHERE (ss_store_sk IS NULL)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (ss_item_sk = i_item_sk)
+UNION ALL    SELECT
+     'web' channel
+   , 'ws_ship_customer_sk' col_name
+   , d_year
+   , d_qoy
+   , i_category
+   , ws_ext_sales_price ext_sales_price
+   FROM
+     web_sales
+   , item
+   , date_dim
+   WHERE (ws_ship_customer_sk IS NULL)
+      AND (ws_sold_date_sk = d_date_sk)
+      AND (ws_item_sk = i_item_sk)
+UNION ALL    SELECT
+     'catalog' channel
+   , 'cs_ship_addr_sk' col_name
+   , d_year
+   , d_qoy
+   , i_category
+   , cs_ext_sales_price ext_sales_price
+   FROM
+     catalog_sales
+   , item
+   , date_dim
+   WHERE (cs_ship_addr_sk IS NULL)
+      AND (cs_sold_date_sk = d_date_sk)
+      AND (cs_item_sk = i_item_sk)
+)  foo
+GROUP BY channel, col_name, d_year, d_qoy, i_category
+ORDER BY channel ASC, col_name ASC, d_year ASC, d_qoy ASC, i_category ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q77.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q77.sql
new file mode 100644
index 0000000000..97062caec7
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q77.sql
@@ -0,0 +1,120 @@
+WITH
+  ss AS (
+   SELECT
+     s_store_sk
+   , sum(ss_ext_sales_price) sales
+   , sum(ss_net_profit) profit
+   FROM
+     store_sales
+   , date_dim
+   , store
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (ss_store_sk = s_store_sk)
+   GROUP BY s_store_sk
+)
+, sr AS (
+   SELECT
+     s_store_sk
+   , sum(sr_return_amt) returns
+   , sum(sr_net_loss) profit_loss
+   FROM
+     store_returns
+   , date_dim
+   , store
+   WHERE (sr_returned_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (sr_store_sk = s_store_sk)
+   GROUP BY s_store_sk
+)
+, cs AS (
+   SELECT
+     cs_call_center_sk
+   , sum(cs_ext_sales_price) sales
+   , sum(cs_net_profit) profit
+   FROM
+     catalog_sales
+   , date_dim
+   WHERE (cs_sold_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+   GROUP BY cs_call_center_sk
+)
+, cr AS (
+   SELECT
+     cr_call_center_sk
+   , sum(cr_return_amount) returns
+   , sum(cr_net_loss) profit_loss
+   FROM
+     catalog_returns
+   , date_dim
+   WHERE (cr_returned_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+   GROUP BY cr_call_center_sk
+)
+, ws AS (
+   SELECT
+     wp_web_page_sk
+   , sum(ws_ext_sales_price) sales
+   , sum(ws_net_profit) profit
+   FROM
+     web_sales
+   , date_dim
+   , web_page
+   WHERE (ws_sold_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (ws_web_page_sk = wp_web_page_sk)
+   GROUP BY wp_web_page_sk
+)
+, wr AS (
+   SELECT
+     wp_web_page_sk
+   , sum(wr_return_amt) returns
+   , sum(wr_net_loss) profit_loss
+   FROM
+     web_returns
+   , date_dim
+   , web_page
+   WHERE (wr_returned_date_sk = d_date_sk)
+      AND (d_date BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (wr_web_page_sk = wp_web_page_sk)
+   GROUP BY wp_web_page_sk
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  channel
+, id
+, sum(sales) sales
+, sum(returns) returns
+, sum(profit) profit
+FROM
+  (
+   SELECT
+     'store channel' channel
+   , ss.s_store_sk id
+   , sales
+   , COALESCE(returns, 0) returns
+   , (profit - COALESCE(profit_loss, 0)) profit
+   FROM
+     ss
+   LEFT JOIN sr ON (ss.s_store_sk = sr.s_store_sk)
+UNION ALL    SELECT
+     'catalog channel' channel
+   , cs_call_center_sk id
+   , sales
+   , returns
+   , (profit - profit_loss) profit
+   FROM
+     cs
+   , cr
+UNION ALL    SELECT
+     'web channel' channel
+   , ws.wp_web_page_sk id
+   , sales
+   , COALESCE(returns, 0) returns
+   , (profit - COALESCE(profit_loss, 0)) profit
+   FROM
+     ws
+   LEFT JOIN wr ON (ws.wp_web_page_sk = wr.wp_web_page_sk)
+)  x
+GROUP BY ROLLUP (channel, id)
+ORDER BY channel ASC, id ASC, sales ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q78.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q78.sql
new file mode 100644
index 0000000000..122a521490
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q78.sql
@@ -0,0 +1,76 @@
+-- at current, run q78 will get wrong result, still under fixing
+/*
+WITH
+  ws AS (
+   SELECT
+     d_year ws_sold_year
+   , ws_item_sk
+   , ws_bill_customer_sk ws_customer_sk
+   , sum(ws_quantity) ws_qty
+   , sum(ws_wholesale_cost) ws_wc
+   , sum(ws_sales_price) ws_sp
+   FROM
+     web_sales
+   LEFT JOIN web_returns ON (wr_order_number = ws_order_number)
+      AND (ws_item_sk = wr_item_sk)
+   INNER JOIN date_dim ON (ws_sold_date_sk = d_date_sk)
+   WHERE (wr_order_number IS NULL)
+   GROUP BY d_year, ws_item_sk, ws_bill_customer_sk
+)
+, cs AS (
+   SELECT
+     d_year cs_sold_year
+   , cs_item_sk
+   , cs_bill_customer_sk cs_customer_sk
+   , sum(cs_quantity) cs_qty
+   , sum(cs_wholesale_cost) cs_wc
+   , sum(cs_sales_price) cs_sp
+   FROM
+     catalog_sales
+   LEFT JOIN catalog_returns ON (cr_order_number = cs_order_number)
+      AND (cs_item_sk = cr_item_sk)
+   INNER JOIN date_dim ON (cs_sold_date_sk = d_date_sk)
+   WHERE (cr_order_number IS NULL)
+   GROUP BY d_year, cs_item_sk, cs_bill_customer_sk
+)
+, ss AS (
+   SELECT
+     d_year ss_sold_year
+   , ss_item_sk
+   , ss_customer_sk
+   , sum(ss_quantity) ss_qty
+   , sum(ss_wholesale_cost) ss_wc
+   , sum(ss_sales_price) ss_sp
+   FROM
+     store_sales
+   LEFT JOIN store_returns ON (sr_ticket_number = ss_ticket_number)
+      AND (ss_item_sk = sr_item_sk)
+   INNER JOIN date_dim ON (ss_sold_date_sk = d_date_sk)
+   WHERE (sr_ticket_number IS NULL)
+   GROUP BY d_year, ss_item_sk, ss_customer_sk
+)
+SELECT
+  ss_sold_year
+, ss_item_sk
+, ss_customer_sk
+, round((ss_qty / COALESCE((ws_qty + cs_qty), 1)), 2) ratio
+, ss_qty store_qty
+, ss_wc store_wholesale_cost
+, ss_sp store_sales_price
+, (COALESCE(ws_qty, 0) + COALESCE(cs_qty, 0)) other_chan_qty
+, (COALESCE(ws_wc, 0) + COALESCE(cs_wc, 0)) other_chan_wholesale_cost
+, (COALESCE(ws_sp, 0) + COALESCE(cs_sp, 0)) other_chan_sales_price
+FROM
+  ss
+LEFT JOIN ws ON (ws_sold_year = ss_sold_year)
+   AND (ws_item_sk = ss_item_sk)
+   AND (ws_customer_sk = ss_customer_sk)
+LEFT JOIN cs ON (cs_sold_year = ss_sold_year)
+   AND (cs_item_sk = cs_item_sk)
+   AND (cs_customer_sk = ss_customer_sk)
+WHERE (COALESCE(ws_qty, 0) > 0)
+   AND (COALESCE(cs_qty, 0) > 0)
+   AND (ss_sold_year = 2000)
+ORDER BY ss_sold_year ASC, ss_item_sk ASC, ss_customer_sk ASC, ss_qty DESC, ss_wc DESC, ss_sp DESC, other_chan_qty ASC, other_chan_wholesale_cost ASC, other_chan_sales_price ASC, round((ss_qty / COALESCE((ws_qty + cs_qty), 1)), 2) ASC
+LIMIT 100
+*/
\ No newline at end of file
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q79.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q79.sql
new file mode 100644
index 0000000000..9af19ab004
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q79.sql
@@ -0,0 +1,34 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_last_name
+, c_first_name
+, substr(s_city, 1, 30)
+, ss_ticket_number
+, amt
+, profit
+FROM
+  (
+   SELECT
+     ss_ticket_number
+   , ss_customer_sk
+   , store.s_city
+   , sum(ss_coupon_amt) amt
+   , sum(ss_net_profit) profit
+   FROM
+     store_sales
+   , date_dim
+   , store
+   , household_demographics
+   WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+      AND (store_sales.ss_store_sk = store.s_store_sk)
+      AND (store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND ((household_demographics.hd_dep_count = 6)
+         OR (household_demographics.hd_vehicle_count > 2))
+      AND (date_dim.d_dow = 1)
+      AND (date_dim.d_year IN (1999   , (1999 + 1)   , (1999 + 2)))
+      AND (store.s_number_employees BETWEEN 200 AND 295)
+   GROUP BY ss_ticket_number, ss_customer_sk, ss_addr_sk, store.s_city
+)  ms
+, customer
+WHERE (ss_customer_sk = c_customer_sk)
+ORDER BY c_last_name ASC, c_first_name ASC, substr(s_city, 1, 30) ASC, profit ASC, ss_ticket_number, amt
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q80.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q80.sql
new file mode 100644
index 0000000000..5d3e772023
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q80.sql
@@ -0,0 +1,106 @@
+WITH
+  ssr AS (
+   SELECT
+     s_store_id store_id
+   , sum(ss_ext_sales_price) sales
+   , sum(COALESCE(sr_return_amt, 0)) returns
+   , sum((ss_net_profit - COALESCE(sr_net_loss, 0))) profit
+   FROM
+     store_sales
+   LEFT JOIN store_returns ON (ss_item_sk = sr_item_sk)
+      AND (ss_ticket_number = sr_ticket_number)
+   , date_dim
+   , store
+   , item
+   , promotion
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (CAST(d_date AS DATE) BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (ss_store_sk = s_store_sk)
+      AND (ss_item_sk = i_item_sk)
+      AND (i_current_price > 50)
+      AND (ss_promo_sk = p_promo_sk)
+      AND (p_channel_tv = 'N')
+   GROUP BY s_store_id
+)
+, csr AS (
+   SELECT
+     cp_catalog_page_id catalog_page_id
+   , sum(cs_ext_sales_price) sales
+   , sum(COALESCE(cr_return_amount, 0)) returns
+   , sum((cs_net_profit - COALESCE(cr_net_loss, 0))) profit
+   FROM
+     catalog_sales
+   LEFT JOIN catalog_returns ON (cs_item_sk = cr_item_sk)
+      AND (cs_order_number = cr_order_number)
+   , date_dim
+   , catalog_page
+   , item
+   , promotion
+   WHERE (cs_sold_date_sk = d_date_sk)
+      AND (CAST(d_date AS DATE) BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (cs_catalog_page_sk = cp_catalog_page_sk)
+      AND (cs_item_sk = i_item_sk)
+      AND (i_current_price > 50)
+      AND (cs_promo_sk = p_promo_sk)
+      AND (p_channel_tv = 'N')
+   GROUP BY cp_catalog_page_id
+)
+, wsr AS (
+   SELECT
+     web_site_id
+   , sum(ws_ext_sales_price) sales
+   , sum(COALESCE(wr_return_amt, 0)) returns
+   , sum((ws_net_profit - COALESCE(wr_net_loss, 0))) profit
+   FROM
+     web_sales
+   LEFT JOIN web_returns ON (ws_item_sk = wr_item_sk)
+      AND (ws_order_number = wr_order_number)
+   , date_dim
+   , web_site
+   , item
+   , promotion
+   WHERE (ws_sold_date_sk = d_date_sk)
+      AND (CAST(d_date AS DATE) BETWEEN CAST('2000-08-23' AS DATE) AND (CAST('2000-08-23' AS DATE) + INTERVAL  '30' DAY))
+      AND (ws_web_site_sk = web_site_sk)
+      AND (ws_item_sk = i_item_sk)
+      AND (i_current_price > 50)
+      AND (ws_promo_sk = p_promo_sk)
+      AND (p_channel_tv = 'N')
+   GROUP BY web_site_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  channel
+, id
+, sum(sales) sales
+, sum(returns) returns
+, sum(profit) profit
+FROM
+  (
+   SELECT
+     'store channel' channel
+   , concat('store', store_id) id
+   , sales
+   , returns
+   , profit
+   FROM
+     ssr
+UNION ALL    SELECT
+     'catalog channel' channel
+   , concat('catalog_page', catalog_page_id) id
+   , sales
+   , returns
+   , profit
+   FROM
+     csr
+UNION ALL    SELECT
+     'web channel' channel
+   , concat('web_site', web_site_id) id
+   , sales
+   , returns
+   , profit
+   FROM
+     wsr
+)  x
+GROUP BY ROLLUP (channel, id)
+ORDER BY channel ASC, id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q81.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q81.sql
new file mode 100644
index 0000000000..4ab02234d2
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q81.sql
@@ -0,0 +1,47 @@
+WITH
+  customer_total_return AS (
+   SELECT
+     cr_returning_customer_sk ctr_customer_sk
+   , ca_state ctr_state
+   , sum(cr_return_amt_inc_tax) ctr_total_return
+   FROM
+     catalog_returns
+   , date_dim
+   , customer_address
+   WHERE (cr_returned_date_sk = d_date_sk)
+      AND (d_year = 2000)
+      AND (cr_returning_addr_sk = ca_address_sk)
+   GROUP BY cr_returning_customer_sk, ca_state
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_customer_id
+, c_salutation
+, c_first_name
+, c_last_name
+, ca_street_number
+, ca_street_name
+, ca_street_type
+, ca_suite_number
+, ca_city
+, ca_county
+, ca_state
+, ca_zip
+, ca_country
+, ca_gmt_offset
+, ca_location_type
+, ctr_total_return
+FROM
+  customer_total_return ctr1
+, customer_address
+, customer
+WHERE (ctr1.ctr_total_return > (
+      SELECT (avg(ctr_total_return) * 1.2)
+      FROM
+        customer_total_return ctr2
+      WHERE (ctr1.ctr_state = ctr2.ctr_state)
+   ))
+   AND (ca_address_sk = c_current_addr_sk)
+   AND (ca_state = 'GA')
+   AND (ctr1.ctr_customer_sk = c_customer_sk)
+ORDER BY c_customer_id ASC, c_salutation ASC, c_first_name ASC, c_last_name ASC, ca_street_number ASC, ca_street_name ASC, ca_street_type ASC, ca_suite_number ASC, ca_city ASC, ca_county ASC, ca_state ASC, ca_zip ASC, ca_country ASC, ca_gmt_offset ASC, ca_location_type ASC, ctr_total_return ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q82.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q82.sql
new file mode 100644
index 0000000000..14a5daa817
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q82.sql
@@ -0,0 +1,19 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, i_current_price
+FROM
+  item
+, inventory
+, date_dim
+, store_sales
+WHERE (i_current_price BETWEEN 62 AND (62 + 30))
+   AND (inv_item_sk = i_item_sk)
+   AND (d_date_sk = inv_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN CAST('2000-05-25' AS DATE) AND (CAST('2000-05-25' AS DATE) + INTERVAL  '60' DAY))
+   AND (i_manufact_id IN (129, 270, 821, 423))
+   AND (inv_quantity_on_hand BETWEEN 100 AND 500)
+   AND (ss_item_sk = i_item_sk)
+GROUP BY i_item_id, i_item_desc, i_current_price
+ORDER BY i_item_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q83.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q83.sql
new file mode 100644
index 0000000000..4b03e0cf22
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q83.sql
@@ -0,0 +1,87 @@
+WITH
+  sr_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(sr_return_quantity) sr_item_qty
+   FROM
+     store_returns
+   , item
+   , date_dim
+   WHERE (sr_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq IN (
+         SELECT d_week_seq
+         FROM
+           date_dim
+         WHERE (d_date IN (CAST('2000-06-30' AS DATE)         , CAST('2000-09-27' AS DATE)         , CAST('2000-11-17' AS DATE)))
+      ))
+   ))
+      AND (sr_returned_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+, cr_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(cr_return_quantity) cr_item_qty
+   FROM
+     catalog_returns
+   , item
+   , date_dim
+   WHERE (cr_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq IN (
+         SELECT d_week_seq
+         FROM
+           date_dim
+         WHERE (d_date IN (CAST('2000-06-30' AS DATE)         , CAST('2000-09-27' AS DATE)         , CAST('2000-11-17' AS DATE)))
+      ))
+   ))
+      AND (cr_returned_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+, wr_items AS (
+   SELECT
+     i_item_id item_id
+   , sum(wr_return_quantity) wr_item_qty
+   FROM
+     web_returns
+   , item
+   , date_dim
+   WHERE (wr_item_sk = i_item_sk)
+      AND (d_date IN (
+      SELECT d_date
+      FROM
+        date_dim
+      WHERE (d_week_seq IN (
+         SELECT d_week_seq
+         FROM
+           date_dim
+         WHERE (d_date IN (CAST('2000-06-30' AS DATE)         , CAST('2000-09-27' AS DATE)         , CAST('2000-11-17' AS DATE)))
+      ))
+   ))
+      AND (wr_returned_date_sk = d_date_sk)
+   GROUP BY i_item_id
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  sr_items.item_id
+, sr_item_qty
+, ((sr_item_qty / ((sr_item_qty + cr_item_qty) + wr_item_qty)) / 3.0) * 100 sr_dev
+, cr_item_qty
+, ((cr_item_qty / ((sr_item_qty + cr_item_qty) + wr_item_qty)) / 3.0) * 100 cr_dev
+, wr_item_qty
+, ((wr_item_qty / ((sr_item_qty + cr_item_qty) + wr_item_qty)) / 3.0) * 100 wr_dev
+, (((sr_item_qty + cr_item_qty) + wr_item_qty) / 3.00) average
+FROM
+  sr_items
+, cr_items
+, wr_items
+WHERE (sr_items.item_id = cr_items.item_id)
+   AND (sr_items.item_id = wr_items.item_id)
+ORDER BY sr_items.item_id ASC, sr_item_qty ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q84.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q84.sql
new file mode 100644
index 0000000000..89daf682af
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q84.sql
@@ -0,0 +1,20 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  c_customer_id customer_id
+, concat(concat(c_last_name, ', '), c_first_name) customername
+FROM
+  customer
+, customer_address
+, customer_demographics
+, household_demographics
+, income_band
+, store_returns
+WHERE (ca_city = 'Edgewood')
+   AND (c_current_addr_sk = ca_address_sk)
+   AND (ib_lower_bound >= 38128)
+   AND (ib_upper_bound <= (38128 + 50000))
+   AND (ib_income_band_sk = hd_income_band_sk)
+   AND (cd_demo_sk = c_current_cdemo_sk)
+   AND (hd_demo_sk = c_current_hdemo_sk)
+   AND (sr_cdemo_sk = cd_demo_sk)
+ORDER BY c_customer_id ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q85.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q85.sql
new file mode 100644
index 0000000000..ff41d7b336
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q85.sql
@@ -0,0 +1,50 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  substr(r_reason_desc, 1, 20)
+, avg(ws_quantity)
+, avg(wr_refunded_cash)
+, avg(wr_fee)
+FROM
+  web_sales
+, web_returns
+, web_page
+, customer_demographics cd1
+, customer_demographics cd2
+, customer_address
+, date_dim
+, reason
+WHERE (ws_web_page_sk = wp_web_page_sk)
+   AND (ws_item_sk = wr_item_sk)
+   AND (ws_order_number = wr_order_number)
+   AND (ws_sold_date_sk = d_date_sk)
+   AND (d_year = 2000)
+   AND (cd1.cd_demo_sk = wr_refunded_cdemo_sk)
+   AND (cd2.cd_demo_sk = wr_returning_cdemo_sk)
+   AND (ca_address_sk = wr_refunded_addr_sk)
+   AND (r_reason_sk = wr_reason_sk)
+   AND (((cd1.cd_marital_status = 'M')
+         AND (cd1.cd_marital_status = cd2.cd_marital_status)
+         AND (cd1.cd_education_status = 'Advanced Degree')
+         AND (cd1.cd_education_status = cd2.cd_education_status)
+         AND (ws_sales_price BETWEEN 100.00 AND 150.00))
+      OR ((cd1.cd_marital_status = 'S')
+         AND (cd1.cd_marital_status = cd2.cd_marital_status)
+         AND (cd1.cd_education_status = 'College')
+         AND (cd1.cd_education_status = cd2.cd_education_status)
+         AND (ws_sales_price BETWEEN 50.00 AND 100.00))
+      OR ((cd1.cd_marital_status = 'W')
+         AND (cd1.cd_marital_status = cd2.cd_marital_status)
+         AND (cd1.cd_education_status = '2 yr Degree')
+         AND (cd1.cd_education_status = cd2.cd_education_status)
+         AND (ws_sales_price BETWEEN 150.00 AND 200.00)))
+   AND (((ca_country = 'United States')
+         AND (ca_state IN ('IN'      , 'OH'      , 'NJ'))
+         AND (ws_net_profit BETWEEN 100 AND 200))
+      OR ((ca_country = 'United States')
+         AND (ca_state IN ('WI'      , 'CT'      , 'KY'))
+         AND (ws_net_profit BETWEEN 150 AND 300))
+      OR ((ca_country = 'United States')
+         AND (ca_state IN ('LA'      , 'IA'      , 'AR'))
+         AND (ws_net_profit BETWEEN 50 AND 250)))
+GROUP BY r_reason_desc
+ORDER BY substr(r_reason_desc, 1, 20) ASC, avg(ws_quantity) ASC, avg(wr_refunded_cash) ASC, avg(wr_fee) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q86.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q86.sql
new file mode 100644
index 0000000000..f19b529d9f
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q86.sql
@@ -0,0 +1,16 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  sum(ws_net_paid) total_sum
+, i_category
+, i_class
+, (GROUPING (i_category) + GROUPING (i_class)) lochierarchy
+, rank() OVER (PARTITION BY (GROUPING (i_category) + GROUPING (i_class)), (CASE WHEN (GROUPING (i_class) = 0) THEN i_category END) ORDER BY sum(ws_net_paid) DESC) rank_within_parent
+FROM
+  web_sales
+, date_dim d1
+, item
+WHERE (d1.d_month_seq BETWEEN 1200 AND (1200 + 11))
+   AND (d1.d_date_sk = ws_sold_date_sk)
+   AND (i_item_sk = ws_item_sk)
+GROUP BY ROLLUP (i_category, i_class)
+ORDER BY lochierarchy DESC, (CASE WHEN (lochierarchy = 0) THEN i_category END) ASC, rank_within_parent ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q87.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q87.sql
new file mode 100644
index 0000000000..09e41fc3d4
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q87.sql
@@ -0,0 +1,40 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ count(*)
+FROM
+  (
+(
+      SELECT DISTINCT
+        c_last_name
+      , c_first_name
+      , d_date
+      FROM
+        store_sales
+      , date_dim
+      , customer
+      WHERE (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
+         AND (store_sales.ss_customer_sk = customer.c_customer_sk)
+         AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   ) EXCEPT (
+      SELECT DISTINCT
+        c_last_name
+      , c_first_name
+      , d_date
+      FROM
+        catalog_sales
+      , date_dim
+      , customer
+      WHERE (catalog_sales.cs_sold_date_sk = date_dim.d_date_sk)
+         AND (catalog_sales.cs_bill_customer_sk = customer.c_customer_sk)
+         AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   ) EXCEPT (
+      SELECT DISTINCT
+        c_last_name
+      , c_first_name
+      , d_date
+      FROM
+        web_sales
+      , date_dim
+      , customer
+      WHERE (web_sales.ws_sold_date_sk = date_dim.d_date_sk)
+         AND (web_sales.ws_bill_customer_sk = customer.c_customer_sk)
+         AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   ) )  cool_cust
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q88.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q88.sql
new file mode 100644
index 0000000000..8104aaa04d
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q88.sql
@@ -0,0 +1,162 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT count(*) h8_30_to_9
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 8)
+      AND (time_dim.t_minute >= 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s1
+, (
+   SELECT count(*) h9_to_9_30
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 9)
+      AND (time_dim.t_minute < 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s2
+, (
+   SELECT count(*) h9_30_to_10
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 9)
+      AND (time_dim.t_minute >= 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s3
+, (
+   SELECT count(*) h10_to_10_30
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 10)
+      AND (time_dim.t_minute < 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s4
+, (
+   SELECT count(*) h10_30_to_11
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 10)
+      AND (time_dim.t_minute >= 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s5
+, (
+   SELECT count(*) h11_to_11_30
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 11)
+      AND (time_dim.t_minute < 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s6
+, (
+   SELECT count(*) h11_30_to_12
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 11)
+      AND (time_dim.t_minute >= 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s7
+, (
+   SELECT count(*) h12_to_12_30
+   FROM
+     store_sales
+   , household_demographics
+   , time_dim
+   , store
+   WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+      AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (time_dim.t_hour = 12)
+      AND (time_dim.t_minute < 30)
+      AND (((household_demographics.hd_dep_count = 4)
+            AND (household_demographics.hd_vehicle_count <= (4 + 2)))
+         OR ((household_demographics.hd_dep_count = 2)
+            AND (household_demographics.hd_vehicle_count <= (2 + 2)))
+         OR ((household_demographics.hd_dep_count = 0)
+            AND (household_demographics.hd_vehicle_count <= (0 + 2))))
+      AND (store.s_store_name = 'ese')
+)  s8
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q89.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q89.sql
new file mode 100644
index 0000000000..b852d0c5db
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q89.sql
@@ -0,0 +1,30 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ *
+FROM
+  (
+   SELECT
+     i_category
+   , i_class
+   , i_brand
+   , s_store_name
+   , s_company_name
+   , d_moy
+   , sum(ss_sales_price) sum_sales
+   , avg(sum(ss_sales_price)) OVER (PARTITION BY i_category, i_brand, s_store_name, s_company_name) avg_monthly_sales
+   FROM
+     item
+   , store_sales
+   , date_dim
+   , store
+   WHERE (ss_item_sk = i_item_sk)
+      AND (ss_sold_date_sk = d_date_sk)
+      AND (ss_store_sk = s_store_sk)
+      AND (d_year IN (1999))
+      AND (((i_category IN ('Books'         , 'Electronics'         , 'Sports'))
+            AND (i_class IN ('computers'         , 'stereo'         , 'football')))
+         OR ((i_category IN ('Men'         , 'Jewelry'         , 'Women'))
+            AND (i_class IN ('shirts'         , 'birdal'         , 'dresses'))))
+   GROUP BY i_category, i_class, i_brand, s_store_name, s_company_name, d_moy
+)  tmp1
+WHERE ((CASE WHEN (avg_monthly_sales <> 0) THEN (abs((sum_sales - avg_monthly_sales)) / avg_monthly_sales) ELSE null END) > 0.1)
+ORDER BY (sum_sales - avg_monthly_sales) ASC, s_store_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q90.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q90.sql
new file mode 100644
index 0000000000..fc5df32b45
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q90.sql
@@ -0,0 +1,32 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ (amc / pmc) am_pm_ratio
+FROM
+  (
+   SELECT count(*) amc
+   FROM
+     web_sales
+   , household_demographics
+   , time_dim
+   , web_page
+   WHERE (ws_sold_time_sk = time_dim.t_time_sk)
+      AND (ws_ship_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ws_web_page_sk = web_page.wp_web_page_sk)
+      AND (time_dim.t_hour BETWEEN 8 AND (8 + 1))
+      AND (household_demographics.hd_dep_count = 6)
+      AND (web_page.wp_char_count BETWEEN 5000 AND 5200)
+)  at
+, (
+   SELECT count(*) pmc
+   FROM
+     web_sales
+   , household_demographics
+   , time_dim
+   , web_page
+   WHERE (ws_sold_time_sk = time_dim.t_time_sk)
+      AND (ws_ship_hdemo_sk = household_demographics.hd_demo_sk)
+      AND (ws_web_page_sk = web_page.wp_web_page_sk)
+      AND (time_dim.t_hour BETWEEN 19 AND (19 + 1))
+      AND (household_demographics.hd_dep_count = 6)
+      AND (web_page.wp_char_count BETWEEN 5000 AND 5200)
+)  pt
+ORDER BY am_pm_ratio ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q91.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q91.sql
new file mode 100644
index 0000000000..c14727116e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q91.sql
@@ -0,0 +1,29 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  cc_call_center_id Call_Center
+, cc_name Call_Center_Name
+, cc_manager Manager
+, sum(cr_net_loss) Returns_Loss
+FROM
+  call_center
+, catalog_returns
+, date_dim
+, customer
+, customer_address
+, customer_demographics
+, household_demographics
+WHERE (cr_call_center_sk = cc_call_center_sk)
+   AND (cr_returned_date_sk = d_date_sk)
+   AND (cr_returning_customer_sk = c_customer_sk)
+   AND (cd_demo_sk = c_current_cdemo_sk)
+   AND (hd_demo_sk = c_current_hdemo_sk)
+   AND (ca_address_sk = c_current_addr_sk)
+   AND (d_year = 1998)
+   AND (d_moy = 11)
+   AND (((cd_marital_status = 'M')
+         AND (cd_education_status = 'Unknown'))
+      OR ((cd_marital_status = 'W')
+         AND (cd_education_status = 'Advanced Degree')))
+   AND (hd_buy_potential LIKE 'Unknown%')
+   AND (ca_gmt_offset = -7)
+GROUP BY cc_call_center_id, cc_name, cc_manager, cd_marital_status, cd_education_status
+ORDER BY sum(cr_net_loss) DESC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q92.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q92.sql
new file mode 100644
index 0000000000..a7f5a05d56
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q92.sql
@@ -0,0 +1,20 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ sum(ws_ext_discount_amt) 'Excess Discount Amount'
+FROM
+  web_sales
+, item
+, date_dim
+WHERE (i_manufact_id = 350)
+   AND (i_item_sk = ws_item_sk)
+   AND (d_date BETWEEN CAST('2000-01-27' AS DATE) AND (CAST('2000-01-27' AS DATE) + INTERVAL  '90' DAY))
+   AND (d_date_sk = ws_sold_date_sk)
+   AND (ws_ext_discount_amt > (
+      SELECT (1.3 * avg(ws_ext_discount_amt))
+      FROM
+        web_sales
+      , date_dim
+      WHERE (ws_item_sk = i_item_sk)
+         AND (d_date BETWEEN CAST('2000-01-27' AS DATE) AND (CAST('2000-01-27' AS DATE) + INTERVAL  '90' DAY))
+         AND (d_date_sk = ws_sold_date_sk)
+   ))
+ORDER BY sum(ws_ext_discount_amt) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q93.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q93.sql
new file mode 100644
index 0000000000..bda6c3841e
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q93.sql
@@ -0,0 +1,21 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  ss_customer_sk
+, sum(act_sales) sumsales
+FROM
+  (
+   SELECT
+     ss_item_sk
+   , ss_ticket_number
+   , ss_customer_sk
+   , (CASE WHEN (sr_return_quantity IS NOT NULL) THEN ((ss_quantity - sr_return_quantity) * ss_sales_price) ELSE (ss_quantity * ss_sales_price) END) act_sales
+   FROM
+     store_sales
+   LEFT JOIN store_returns ON (sr_item_sk = ss_item_sk)
+      AND (sr_ticket_number = ss_ticket_number)
+   , reason
+   WHERE (sr_reason_sk = r_reason_sk)
+      AND (r_reason_desc = 'reason 28')
+)  t
+GROUP BY ss_customer_sk
+ORDER BY sumsales ASC, ss_customer_sk ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q94.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q94.sql
new file mode 100644
index 0000000000..d41deca46c
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q94.sql
@@ -0,0 +1,30 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  count(DISTINCT ws_order_number) 'order count'
+, sum(ws_ext_ship_cost) 'total shipping cost'
+, sum(ws_net_profit) 'total net profit'
+FROM
+  web_sales ws1
+, date_dim
+, customer_address
+, web_site
+WHERE (d_date BETWEEN CAST('1999-2-01' AS DATE) AND (CAST('1999-2-01' AS DATE) + INTERVAL  '60' DAY))
+   AND (ws1.ws_ship_date_sk = d_date_sk)
+   AND (ws1.ws_ship_addr_sk = ca_address_sk)
+   AND (ca_state = 'IL')
+   AND (ws1.ws_web_site_sk = web_site_sk)
+   AND (web_company_name = 'pri')
+   AND (EXISTS (
+   SELECT *
+   FROM
+     web_sales ws2
+   WHERE (ws1.ws_order_number = ws2.ws_order_number)
+      AND (ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)
+))
+   AND (NOT (EXISTS (
+   SELECT *
+   FROM
+     web_returns wr1
+   WHERE (ws1.ws_order_number = wr1.wr_order_number)
+)))
+ORDER BY count(DISTINCT ws_order_number) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q95.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q95.sql
new file mode 100644
index 0000000000..1beb8a09f1
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q95.sql
@@ -0,0 +1,41 @@
+WITH
+  ws_wh AS (
+   SELECT
+     ws1.ws_order_number
+   , ws1.ws_warehouse_sk wh1
+   , ws2.ws_warehouse_sk wh2
+   FROM
+     web_sales ws1
+   , web_sales ws2
+   WHERE (ws1.ws_order_number = ws2.ws_order_number)
+      AND (ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk)
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  count(DISTINCT ws_order_number) 'order count'
+, sum(ws_ext_ship_cost) 'total shipping cost'
+, sum(ws_net_profit) 'total net profit'
+FROM
+  web_sales ws1
+, date_dim
+, customer_address
+, web_site
+WHERE (CAST(d_date AS DATE) BETWEEN CAST('1999-2-01' AS DATE) AND (CAST('1999-2-01' AS DATE) + INTERVAL  '60' DAY))
+   AND (ws1.ws_ship_date_sk = d_date_sk)
+   AND (ws1.ws_ship_addr_sk = ca_address_sk)
+   AND (ca_state = 'IL')
+   AND (ws1.ws_web_site_sk = web_site_sk)
+   AND (web_company_name = 'pri')
+   AND (ws1.ws_order_number IN (
+   SELECT ws_order_number
+   FROM
+     ws_wh
+))
+   AND (ws1.ws_order_number IN (
+   SELECT wr_order_number
+   FROM
+     web_returns
+   , ws_wh
+   WHERE (wr_order_number = ws_wh.ws_order_number)
+))
+ORDER BY count(DISTINCT ws_order_number) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q96.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q96.sql
new file mode 100644
index 0000000000..0a904a146b
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q96.sql
@@ -0,0 +1,15 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */ count(*)
+FROM
+  store_sales
+, household_demographics
+, time_dim
+, store
+WHERE (ss_sold_time_sk = time_dim.t_time_sk)
+   AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
+   AND (ss_store_sk = s_store_sk)
+   AND (time_dim.t_hour = 20)
+   AND (time_dim.t_minute >= 30)
+   AND (household_demographics.hd_dep_count = 7)
+   AND (store.s_store_name = 'ese')
+ORDER BY count(*) ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q97.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q97.sql
new file mode 100644
index 0000000000..37ece5aeec
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q97.sql
@@ -0,0 +1,35 @@
+WITH
+  ssci AS (
+   SELECT
+     ss_customer_sk customer_sk
+   , ss_item_sk item_sk
+   FROM
+     store_sales
+   , date_dim
+   WHERE (ss_sold_date_sk = d_date_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   GROUP BY ss_customer_sk, ss_item_sk
+)
+, csci AS (
+   SELECT
+     cs_bill_customer_sk customer_sk
+   , cs_item_sk item_sk
+   FROM
+     catalog_sales
+   , date_dim
+   WHERE (cs_sold_date_sk = d_date_sk)
+      AND (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   GROUP BY cs_bill_customer_sk, cs_item_sk
+)
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  sum((CASE WHEN (ssci.customer_sk IS NOT NULL)
+   AND (csci.customer_sk IS NULL) THEN 1 ELSE 0 END)) store_only
+, sum((CASE WHEN (ssci.customer_sk IS NULL)
+   AND (csci.customer_sk IS NOT NULL) THEN 1 ELSE 0 END)) catalog_only
+, sum((CASE WHEN (ssci.customer_sk IS NOT NULL)
+   AND (csci.customer_sk IS NOT NULL) THEN 1 ELSE 0 END)) store_and_catalog
+FROM
+  ssci
+FULL JOIN csci ON (ssci.customer_sk = csci.customer_sk)
+   AND (ssci.item_sk = csci.item_sk)
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q98.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q98.sql
new file mode 100644
index 0000000000..06cd393e40
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q98.sql
@@ -0,0 +1,18 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  i_item_id
+, i_item_desc
+, i_category
+, i_class
+, i_current_price
+, sum(ss_ext_sales_price) itemrevenue
+, ((sum(ss_ext_sales_price) * 100) / sum(sum(ss_ext_sales_price)) OVER (PARTITION BY i_class)) revenueratio
+FROM
+  store_sales
+, item
+, date_dim
+WHERE (ss_item_sk = i_item_sk)
+   AND (i_category IN ('Sports', 'Books', 'Home'))
+   AND (ss_sold_date_sk = d_date_sk)
+   AND (CAST(d_date AS DATE) BETWEEN CAST('1999-02-22' AS DATE) AND (CAST('1999-02-22' AS DATE) + INTERVAL  '30' DAY))
+GROUP BY i_item_id, i_item_desc, i_category, i_class, i_current_price
+ORDER BY i_category ASC, i_class ASC, i_item_id ASC, i_item_desc ASC, revenueratio ASC
diff --git a/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q99.sql b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q99.sql
new file mode 100644
index 0000000000..4fa75e019f
--- /dev/null
+++ b/regression-test/suites/datev2/tpcds_sf1_p1/sql/pipeline_q99.sql
@@ -0,0 +1,26 @@
+SELECT /*+SET_VAR(enable_pipeline_engine=true) */
+  substr(w_warehouse_name, 1, 20)
+, sm_type
+, cc_name
+, sum((CASE WHEN ((cs_ship_date_sk - cs_sold_date_sk) <= 30) THEN 1 ELSE 0 END)) '30 days'
+, sum((CASE WHEN ((cs_ship_date_sk - cs_sold_date_sk) > 30)
+   AND ((cs_ship_date_sk - cs_sold_date_sk) <= 60) THEN 1 ELSE 0 END)) '31-60 days'
+, sum((CASE WHEN ((cs_ship_date_sk - cs_sold_date_sk) > 60)
+   AND ((cs_ship_date_sk - cs_sold_date_sk) <= 90) THEN 1 ELSE 0 END)) '61-90 days'
+, sum((CASE WHEN ((cs_ship_date_sk - cs_sold_date_sk) > 90)
+   AND ((cs_ship_date_sk - cs_sold_date_sk) <= 120) THEN 1 ELSE 0 END)) '91-120 days'
+, sum((CASE WHEN ((cs_ship_date_sk - cs_sold_date_sk) > 120) THEN 1 ELSE 0 END)) '>120 days'
+FROM
+  catalog_sales
+, warehouse
+, ship_mode
+, call_center
+, date_dim
+WHERE (d_month_seq BETWEEN 1200 AND (1200 + 11))
+   AND (cs_ship_date_sk = d_date_sk)
+   AND (cs_warehouse_sk = w_warehouse_sk)
+   AND (cs_ship_mode_sk = sm_ship_mode_sk)
+   AND (cs_call_center_sk = cc_call_center_sk)
+GROUP BY substr(w_warehouse_name, 1, 20), sm_type, cc_name
+ORDER BY substr(w_warehouse_name, 1, 20) ASC, sm_type ASC, cc_name ASC
+LIMIT 100
diff --git a/regression-test/suites/datev2/tpch_sf1_p1/load.groovy b/regression-test/suites/datev2/tpch_sf1_p1/load.groovy
index fba0f7a849..4f5318d2bf 100644
--- a/regression-test/suites/datev2/tpch_sf1_p1/load.groovy
+++ b/regression-test/suites/datev2/tpch_sf1_p1/load.groovy
@@ -44,7 +44,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split00.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -79,7 +79,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split01.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split01.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/datev2/tpch_sf1_p1/tpch_sf1/load.groovy b/regression-test/suites/datev2/tpch_sf1_p1/tpch_sf1/load.groovy
index c9eed04dc4..ba8381192e 100644
--- a/regression-test/suites/datev2/tpch_sf1_p1/tpch_sf1/load.groovy
+++ b/regression-test/suites/datev2/tpch_sf1_p1/tpch_sf1/load.groovy
@@ -53,7 +53,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split00.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -88,7 +88,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split01.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split01.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/decimalv3/tpch_sf1_p1/load.groovy b/regression-test/suites/decimalv3/tpch_sf1_p1/load.groovy
index 6b29d016ad..e36598bb3b 100644
--- a/regression-test/suites/decimalv3/tpch_sf1_p1/load.groovy
+++ b/regression-test/suites/decimalv3/tpch_sf1_p1/load.groovy
@@ -44,7 +44,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split00.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -78,7 +78,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split01.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split01.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/nereids_datev2_p1/load.groovy b/regression-test/suites/nereids_datev2_p1/load.groovy
index 2190504238..d371220681 100644
--- a/regression-test/suites/nereids_datev2_p1/load.groovy
+++ b/regression-test/suites/nereids_datev2_p1/load.groovy
@@ -60,7 +60,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/tpch/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/nereids_tpch_p1/load.groovy b/regression-test/suites/nereids_tpch_p1/load.groovy
index 072d82496c..6590d69d53 100644
--- a/regression-test/suites/nereids_tpch_p1/load.groovy
+++ b/regression-test/suites/nereids_tpch_p1/load.groovy
@@ -60,7 +60,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/tpch/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/four/load_four_step.groovy b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/four/load_four_step.groovy
index e40a4a8ed8..450ecf48bd 100644
--- a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/four/load_four_step.groovy
+++ b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/four/load_four_step.groovy
@@ -39,7 +39,7 @@ suite("load_four_step") {
 
                 // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
                 // also, you can stream load a http stream, e.g. http://xxx/some.csv
-                file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+                file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
                 
 
                 time 10000 // limit inflight 10s
@@ -97,7 +97,7 @@ suite("load_four_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/one/load_one_step.groovy b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/one/load_one_step.groovy
index ec253abda9..64bb276553 100644
--- a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/one/load_one_step.groovy
+++ b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/one/load_one_step.groovy
@@ -34,7 +34,7 @@ suite("load_one_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/three/load_three_step.groovy b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/three/load_three_step.groovy
index 0f7b3bec69..04ec4b03e2 100644
--- a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/three/load_three_step.groovy
+++ b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/three/load_three_step.groovy
@@ -36,7 +36,7 @@ suite("load_three_step") {
 
                 // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
                 // also, you can stream load a http stream, e.g. http://xxx/some.csv
-                file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+                file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
                 time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/two/load_two_step.groovy b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/two/load_two_step.groovy
index 5aa9a80f26..c49410abb9 100644
--- a/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/two/load_two_step.groovy
+++ b/regression-test/suites/primary_index/ssb_unique_load_zstd_p0/two/load_two_step.groovy
@@ -35,7 +35,7 @@ suite("load_two_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/ssb_unique_sql_zstd_p0/load.groovy b/regression-test/suites/primary_index/ssb_unique_sql_zstd_p0/load.groovy
index dd6bee5e53..417b5fa64c 100644
--- a/regression-test/suites/primary_index/ssb_unique_sql_zstd_p0/load.groovy
+++ b/regression-test/suites/primary_index/ssb_unique_sql_zstd_p0/load.groovy
@@ -56,7 +56,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/primary_index/test_unique_mow_sequence.groovy b/regression-test/suites/primary_index/test_unique_mow_sequence.groovy
index 75919e18b9..e0ac2feca7 100644
--- a/regression-test/suites/primary_index/test_unique_mow_sequence.groovy
+++ b/regression-test/suites/primary_index/test_unique_mow_sequence.groovy
@@ -47,7 +47,7 @@ suite("test_unique_mow_sequence") {
             set 'columns', 'c_custkey,c_name,c_address,c_city,c_nation,c_region,c_phone,c_mktsegment,no_use'
             set 'function_column.sequence_col', 'c_custkey'
 
-            file """${getS3Url()}/ssb/sf0.1/customer.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/customer.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_sf0.1_p1/load.groovy b/regression-test/suites/ssb_sf0.1_p1/load.groovy
index 2372cd6d0f..6baf4d2bb0 100644
--- a/regression-test/suites/ssb_sf0.1_p1/load.groovy
+++ b/regression-test/suites/ssb_sf0.1_p1/load.groovy
@@ -59,7 +59,7 @@ suite("load") {
             set 'columns', columns[i]
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_sf1/load.groovy b/regression-test/suites/ssb_sf1/load.groovy
index b846f2833a..6d2b093c13 100644
--- a/regression-test/suites/ssb_sf1/load.groovy
+++ b/regression-test/suites/ssb_sf1/load.groovy
@@ -59,7 +59,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf1/${tableName}.tbl.split00.gz"""
+            file """${getS3Url()}/regression/ssb/sf1/${tableName}.tbl.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -94,7 +94,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf1/${tableName}.tbl.split01.gz"""
+            file """${getS3Url()}/regression/ssb/sf1/${tableName}.tbl.split01.gz"""
             
 
             time 10000 // limit inflight 10s
diff --git a/regression-test/suites/ssb_sf1_p2/load.groovy b/regression-test/suites/ssb_sf1_p2/load.groovy
index bdd329dd8a..fdaaad5c3b 100644
--- a/regression-test/suites/ssb_sf1_p2/load.groovy
+++ b/regression-test/suites/ssb_sf1_p2/load.groovy
@@ -61,7 +61,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_unique_load_zstd_p0/load_four_step/load.groovy b/regression-test/suites/ssb_unique_load_zstd_p0/load_four_step/load.groovy
index 432b60e8d2..3c6ea2b4ef 100644
--- a/regression-test/suites/ssb_unique_load_zstd_p0/load_four_step/load.groovy
+++ b/regression-test/suites/ssb_unique_load_zstd_p0/load_four_step/load.groovy
@@ -39,7 +39,7 @@ suite("load_four_step") {
 
                 // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
                 // also, you can stream load a http stream, e.g. http://xxx/some.csv
-                file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+                file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
                 time 10000 // limit inflight 10s
 
@@ -81,7 +81,7 @@ suite("load_four_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_unique_load_zstd_p0/load_one_step/load.groovy b/regression-test/suites/ssb_unique_load_zstd_p0/load_one_step/load.groovy
index 838ee1e150..1182f2016f 100644
--- a/regression-test/suites/ssb_unique_load_zstd_p0/load_one_step/load.groovy
+++ b/regression-test/suites/ssb_unique_load_zstd_p0/load_one_step/load.groovy
@@ -36,7 +36,7 @@ suite("load_one_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_unique_load_zstd_p0/load_three_step/load.groovy b/regression-test/suites/ssb_unique_load_zstd_p0/load_three_step/load.groovy
index 572c41dfdf..28cc474d40 100644
--- a/regression-test/suites/ssb_unique_load_zstd_p0/load_three_step/load.groovy
+++ b/regression-test/suites/ssb_unique_load_zstd_p0/load_three_step/load.groovy
@@ -36,7 +36,7 @@ suite("load_three_step") {
 
                 // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
                 // also, you can stream load a http stream, e.g. http://xxx/some.csv
-                file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+                file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
                 time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_unique_load_zstd_p0/load_two_step/load.groovy b/regression-test/suites/ssb_unique_load_zstd_p0/load_two_step/load.groovy
index addf0e943c..fa1f9edbcb 100644
--- a/regression-test/suites/ssb_unique_load_zstd_p0/load_two_step/load.groovy
+++ b/regression-test/suites/ssb_unique_load_zstd_p0/load_two_step/load.groovy
@@ -35,7 +35,7 @@ suite("load_two_step") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/ssb_unique_sql_zstd_p0/load.groovy b/regression-test/suites/ssb_unique_sql_zstd_p0/load.groovy
index bf6b0796a2..1c51fdd56e 100644
--- a/regression-test/suites/ssb_unique_sql_zstd_p0/load.groovy
+++ b/regression-test/suites/ssb_unique_sql_zstd_p0/load.groovy
@@ -56,7 +56,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/ssb/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/ssb/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpcds_sf1_p1/load.groovy b/regression-test/suites/tpcds_sf1_p1/load.groovy
index 92839df67d..d1713217af 100644
--- a/regression-test/suites/tpcds_sf1_p1/load.groovy
+++ b/regression-test/suites/tpcds_sf1_p1/load.groovy
@@ -79,7 +79,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpcds/sf1/${tableName}.dat.gz"""
+            file """${getS3Url()}/regression/tpcds/sf1/${tableName}.dat.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpcds_sf1_unique_p1/load.groovy b/regression-test/suites/tpcds_sf1_unique_p1/load.groovy
index a39613f0a4..c06e1ea61a 100644
--- a/regression-test/suites/tpcds_sf1_unique_p1/load.groovy
+++ b/regression-test/suites/tpcds_sf1_unique_p1/load.groovy
@@ -118,7 +118,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpcds/sf1/${tableName}.dat.gz"""
+            file """${getS3Url()}/regression/tpcds/sf1/${tableName}.dat.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpch_sf0.1_p1/load.groovy b/regression-test/suites/tpch_sf0.1_p1/load.groovy
index f5dc6d9b71..9b8d54a4da 100644
--- a/regression-test/suites/tpch_sf0.1_p1/load.groovy
+++ b/regression-test/suites/tpch_sf0.1_p1/load.groovy
@@ -54,7 +54,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/tpch/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpch_sf0.1_unique_p1/load.groovy b/regression-test/suites/tpch_sf0.1_unique_p1/load.groovy
index f5dc6d9b71..9b8d54a4da 100644
--- a/regression-test/suites/tpch_sf0.1_unique_p1/load.groovy
+++ b/regression-test/suites/tpch_sf0.1_unique_p1/load.groovy
@@ -54,7 +54,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf0.1/${tableName}.tbl.gz"""
+            file """${getS3Url()}/regression/tpch/sf0.1/${tableName}.tbl.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpch_sf1_p2/load.groovy b/regression-test/suites/tpch_sf1_p2/load.groovy
index 26ad7f4c55..a0e4bb618a 100644
--- a/regression-test/suites/tpch_sf1_p2/load.groovy
+++ b/regression-test/suites/tpch_sf1_p2/load.groovy
@@ -54,7 +54,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split00.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -89,7 +89,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split01.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split01.gz"""
 
             time 10000 // limit inflight 10s
 
diff --git a/regression-test/suites/tpch_sf1_unique_p2/load.groovy b/regression-test/suites/tpch_sf1_unique_p2/load.groovy
index 6b29d016ad..e36598bb3b 100644
--- a/regression-test/suites/tpch_sf1_unique_p2/load.groovy
+++ b/regression-test/suites/tpch_sf1_unique_p2/load.groovy
@@ -44,7 +44,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split00.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split00.gz"""
 
             time 10000 // limit inflight 10s
 
@@ -78,7 +78,7 @@ suite("load") {
 
             // relate to ${DORIS_HOME}/regression-test/data/demo/streamload_input.csv.
             // also, you can stream load a http stream, e.g. http://xxx/some.csv
-            file """${getS3Url()}/tpch/sf1/${tableName}.csv.split01.gz"""
+            file """${getS3Url()}/regression/tpch/sf1/${tableName}.csv.split01.gz"""
 
             time 10000 // limit inflight 10s