[fix](nereids) fix UnknownValue's reference in simplify range rule (a…

…pache#44637)

for sql 'a > 10 and b > 20' will generate an UnknowValue with
sourceValues = [ Range(a > 10), Range(b > 20) ].

in UnknowValue's constructor, it set reference =
sourceValues[0].reference, then its reference = a. This is an error.

when A union/interset B generating an UnknowValue, this UnknownValue's
reference should be:
1. A.reference if A.reference = B.reference;
2. expr(A union/interset B)  if A.reference != B.reference;

fe/fe-core/src/test/java/org/apache/doris/nereids/rules/expression/SimplifyRangeTest.java

@@ -101,6 +101,7 @@ public void testSimplify() {
         assertRewrite("TA > 5 + 1 and TA > 10", "cast(TA as smallint) > 6 and TA > 10");
         assertRewrite("(TA > 1 and TA > 10) or TA > 20", "TA > 10");
         assertRewrite("(TA > 1 or TA > 10) and TA > 20", "TA > 20");
+        assertRewrite("(TA < 1 and TA > 10) or TA = 20 and TB > 10", "(TA is null and null) or TA = 20 and TB > 10");
         assertRewrite("(TA + TB > 1 or TA + TB > 10) and TA + TB > 20", "TA + TB > 20");
         assertRewrite("TA > 10 or TA > 10", "TA > 10");
         assertRewrite("(TA > 10 or TA > 20) and (TB > 10 and TB < 20)", "TA > 10 and (TB > 10 and TB < 20) ");
@@ -131,7 +132,10 @@ public void testSimplify() {
         assertRewrite("TA in (1) and TA in (3)", "TA is null and null");
         assertRewrite("TA in (1) and TA in (1)", "TA = 1");
         assertRewriteNotNull("(TA > 3 and TA < 1) and TB < 5", "FALSE");
+        assertRewrite("(TA > 3 and TA < 1) and (TA > 5 and TA = 4)", "TA is null and null");
+        assertRewrite("(TA > 3 and TA < 1) or (TA > 5 and TA = 4)", "TA is null and null");
         assertRewrite("(TA > 3 and TA < 1) and TB < 5", "TA is null and null and TB < 5");
+        assertRewrite("(TA > 3 and TA < 1) and (TB < 5 and TB = 6)", "TA is null and null and TB is null");
         assertRewrite("TA > 3 and TB < 5 and TA < 1", "TA is null and null and TB < 5");
         assertRewrite("(TA > 3 and TA < 1) or TB < 5", "(TA is null and null) or TB < 5");
         assertRewrite("((IA = 1 AND SC ='1') OR SC = '1212') AND IA =1", "((IA = 1 AND SC ='1') OR SC = '1212') AND IA =1");

fe/fe-core/src/test/java/org/apache/doris/nereids/rules/rewrite/OrToInTest.java

@@ -193,7 +193,7 @@ void test15() {
         String expr = "x=1 or (a=1 and b=2) or (a=2 and c=3)";
         Expression expression = PARSER.parseExpression(expr);
         Expression rewritten = OrToIn.INSTANCE.rewriteTree(expression, context);
-        Assertions.assertEquals("((x = 1) OR (((a = 1) AND (b = 2)) OR ((a = 2) AND (c = 3))))",
+        Assertions.assertEquals("(((x = 1) OR ((a = 1) AND (b = 2))) OR ((a = 2) AND (c = 3)))",
                 rewritten.toSql());
     }
 

regression-test/data/nereids_hint_tpcds_p0/shape/query41.out

@@ -18,6 +18,6 @@ PhysicalResultSink
 ------------------------PhysicalDistribute[DistributionSpecHash]
 --------------------------hashAgg[LOCAL]
 ----------------------------PhysicalProject
-------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND ((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR (((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large')) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small'))))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND ((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR (((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large')) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small')))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
+------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND (((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small')))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
 --------------------------------PhysicalOlapScan[item]
 

regression-test/data/nereids_hint_tpcds_p0/shape/query47.out

@@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 )
 ------------------------------------PhysicalProject
 --------------------------------------PhysicalOlapScan[store_sales] apply RFs: RF0 RF1 RF2
 ------------------------------------PhysicalProject
---------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001) and d_year IN (1999, 2000, 2001))
+--------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001))
 ----------------------------------------PhysicalOlapScan[date_dim]
 --------------------------------PhysicalProject
 ----------------------------------PhysicalOlapScan[store]

regression-test/data/nereids_hint_tpcds_p0/shape/query88.out

@@ -24,7 +24,7 @@ PhysicalResultSink
 --------------------------------------filter((time_dim.t_hour = 8) and (time_dim.t_minute >= 30))
 ----------------------------------------PhysicalOlapScan[time_dim]
 --------------------------------PhysicalProject
-----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ------------------------------------PhysicalOlapScan[household_demographics]
 ----------------------------PhysicalProject
 ------------------------------filter((store.s_store_name = 'ese'))
@@ -45,7 +45,7 @@ PhysicalResultSink
 --------------------------------------filter((time_dim.t_hour = 9) and (time_dim.t_minute < 30))
 ----------------------------------------PhysicalOlapScan[time_dim]
 --------------------------------PhysicalProject
-----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ------------------------------------PhysicalOlapScan[household_demographics]
 ----------------------------PhysicalProject
 ------------------------------filter((store.s_store_name = 'ese'))
@@ -66,7 +66,7 @@ PhysicalResultSink
 ------------------------------------filter((time_dim.t_hour = 9) and (time_dim.t_minute >= 30))
 --------------------------------------PhysicalOlapScan[time_dim]
 ------------------------------PhysicalProject
---------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+--------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ----------------------------------PhysicalOlapScan[household_demographics]
 --------------------------PhysicalProject
 ----------------------------filter((store.s_store_name = 'ese'))
@@ -87,7 +87,7 @@ PhysicalResultSink
 ----------------------------------filter((time_dim.t_hour = 10) and (time_dim.t_minute < 30))
 ------------------------------------PhysicalOlapScan[time_dim]
 ----------------------------PhysicalProject
-------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 --------------------------------PhysicalOlapScan[household_demographics]
 ------------------------PhysicalProject
 --------------------------filter((store.s_store_name = 'ese'))
@@ -108,7 +108,7 @@ PhysicalResultSink
 --------------------------------filter((time_dim.t_hour = 10) and (time_dim.t_minute >= 30))
 ----------------------------------PhysicalOlapScan[time_dim]
 --------------------------PhysicalProject
-----------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+----------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ------------------------------PhysicalOlapScan[household_demographics]
 ----------------------PhysicalProject
 ------------------------filter((store.s_store_name = 'ese'))
@@ -129,7 +129,7 @@ PhysicalResultSink
 ------------------------------filter((time_dim.t_hour = 11) and (time_dim.t_minute < 30))
 --------------------------------PhysicalOlapScan[time_dim]
 ------------------------PhysicalProject
---------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+--------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ----------------------------PhysicalOlapScan[household_demographics]
 --------------------PhysicalProject
 ----------------------filter((store.s_store_name = 'ese'))
@@ -150,7 +150,7 @@ PhysicalResultSink
 ----------------------------filter((time_dim.t_hour = 11) and (time_dim.t_minute >= 30))
 ------------------------------PhysicalOlapScan[time_dim]
 ----------------------PhysicalProject
-------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 --------------------------PhysicalOlapScan[household_demographics]
 ------------------PhysicalProject
 --------------------filter((store.s_store_name = 'ese'))
@@ -171,7 +171,7 @@ PhysicalResultSink
 --------------------------filter((time_dim.t_hour = 12) and (time_dim.t_minute < 30))
 ----------------------------PhysicalOlapScan[time_dim]
 --------------------PhysicalProject
-----------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3) and hd_dep_count IN (-1, 0, 3))
+----------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
 ------------------------PhysicalOlapScan[household_demographics]
 ----------------PhysicalProject
 ------------------filter((store.s_store_name = 'ese'))

regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out

@@ -18,6 +18,6 @@ PhysicalResultSink
 ------------------------PhysicalDistribute[DistributionSpecHash]
 --------------------------hashAgg[LOCAL]
 ----------------------------PhysicalProject
-------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND ((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR (((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large')) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small'))))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND ((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR (((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large')) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small')))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
+------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND (((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small')))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
 --------------------------------PhysicalOlapScan[item]
 

regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out

@@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 )
 ------------------------------------PhysicalProject
 --------------------------------------PhysicalOlapScan[store_sales] apply RFs: RF0 RF1 RF2
 ------------------------------------PhysicalProject
---------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001) and d_year IN (1999, 2000, 2001))
+--------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001))
 ----------------------------------------PhysicalOlapScan[date_dim]
 --------------------------------PhysicalProject
 ----------------------------------PhysicalOlapScan[store]