fix: Normalize NaN and zeros for floating number comparison (apache#953)

* fix: Normalize NaN and zeros for floating number comparison

* fix

* fix

* update

* Add doc

docs/source/user-guide/compatibility.md

@@ -38,6 +38,14 @@ Comet uses the Rust regexp crate for evaluating regular expressions, and this ha
 regular expression engine. Comet will fall back to Spark for patterns that are known to produce different results, but
 this can be overridden by setting `spark.comet.regexp.allowIncompatible=true`.
 
+## Floating number comparison
+
+Spark normalizes NaN and zero for floating point numbers for several cases. See `NormalizeFloatingNumbers` optimization rule in Spark.
+However, one exception is comparison. Spark does not normalize NaN and zero when comparing values
+because they are handled well in Spark (e.g., `SQLOrderingUtil.compareFloats`). But the comparison
+functions of arrow-rs used by DataFusion do not normalize NaN and zero (e.g., [arrow::compute::kernels::cmp::eq](https://docs.rs/arrow/latest/arrow/compute/kernels/cmp/fn.eq.html#)).
+So Comet will add additional normalization expression of NaN and zero for comparison.
+
 ## Cast
 
 Cast operations in Comet fall into three levels of support:

spark/src/main/scala/org/apache/comet/CometSparkSessionExtensions.scala

@@ -25,8 +25,9 @@ import org.apache.spark.SparkConf
 import org.apache.spark.internal.Logging
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.sql.{SparkSession, SparkSessionExtensions}
-import org.apache.spark.sql.catalyst.expressions.{Expression, PlanExpression}
+import org.apache.spark.sql.catalyst.expressions.{EqualNullSafe, EqualTo, Expression, GreaterThan, GreaterThanOrEqual, KnownFloatingPointNormalized, LessThan, LessThanOrEqual, NamedExpression, PlanExpression}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{Final, Partial}
+import org.apache.spark.sql.catalyst.optimizer.NormalizeNaNAndZero
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.comet._
@@ -47,6 +48,7 @@ import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, ReusedExc
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, ShuffledHashJoinExec, SortMergeJoinExec}
 import org.apache.spark.sql.execution.window.WindowExec
 import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{DoubleType, FloatType}
 
 import org.apache.comet.CometConf._
 import org.apache.comet.CometExplainInfo.getActualPlan
@@ -840,6 +842,52 @@ class CometSparkSessionExtensions
       }
     }
 
+    def normalizePlan(plan: SparkPlan): SparkPlan = {
+      plan.transformUp {
+        case p: ProjectExec =>
+          val newProjectList = p.projectList.map(normalize(_).asInstanceOf[NamedExpression])
+          ProjectExec(newProjectList, p.child)
+        case f: FilterExec =>
+          val newCondition = normalize(f.condition)
+          FilterExec(newCondition, f.child)
+      }
+    }
+
+    // Spark will normalize NaN and zero for floating point numbers for several cases.
+    // See `NormalizeFloatingNumbers` optimization rule in Spark.
+    // However, one exception is for comparison operators. Spark does not normalize NaN and zero
+    // because they are handled well in Spark (e.g., `SQLOrderingUtil.compareFloats`). But the
+    // comparison functions in arrow-rs do not normalize NaN and zero. So we need to normalize NaN
+    // and zero for comparison operators in Comet.
+    def normalize(expr: Expression): Expression = {
+      expr.transformUp {
+        case EqualTo(left, right) =>
+          EqualTo(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+        case EqualNullSafe(left, right) =>
+          EqualNullSafe(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+        case GreaterThan(left, right) =>
+          GreaterThan(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+        case GreaterThanOrEqual(left, right) =>
+          GreaterThanOrEqual(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+        case LessThan(left, right) =>
+          LessThan(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+        case LessThanOrEqual(left, right) =>
+          LessThanOrEqual(normalizeNaNAndZero(left), normalizeNaNAndZero(right))
+      }
+    }
+
+    def normalizeNaNAndZero(expr: Expression): Expression = {
+      expr match {
+        case _: KnownFloatingPointNormalized => expr
+        case _ =>
+          expr.dataType match {
+            case _: FloatType | _: DoubleType =>
+              KnownFloatingPointNormalized(NormalizeNaNAndZero(expr))
+            case _ => expr
+          }
+      }
+    }
+
     override def apply(plan: SparkPlan): SparkPlan = {
       // DataFusion doesn't have ANSI mode. For now we just disable CometExec if ANSI mode is
       // enabled.
@@ -865,7 +913,7 @@ class CometSparkSessionExtensions
           plan
         }
       } else {
-        var newPlan = transform(plan)
+        var newPlan = transform(normalizePlan(plan))
 
         // if the plan cannot be run fully natively then explain why (when appropriate
         // config is enabled)

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark3_5/q21/explain.txt

@@ -129,7 +129,7 @@ Functions [2]: [sum(CASE WHEN (d_date#12 < 2000-03-11) THEN inv_quantity_on_hand
 
 (22) CometFilter
 Input [4]: [w_warehouse_name#7, i_item_id#9, inv_before#15, inv_after#16]
-Condition : (CASE WHEN (inv_before#15 > 0) THEN ((cast(inv_after#16 as double) / cast(inv_before#15 as double)) >= 0.666667) END AND CASE WHEN (inv_before#15 > 0) THEN ((cast(inv_after#16 as double) / cast(inv_before#15 as double)) <= 1.5) END)
+Condition : (CASE WHEN (inv_before#15 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(inv_after#16 as double) / cast(inv_before#15 as double)))) >= knownfloatingpointnormalized(normalizenanandzero(0.666667))) END AND CASE WHEN (inv_before#15 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(inv_after#16 as double) / cast(inv_before#15 as double)))) <= knownfloatingpointnormalized(normalizenanandzero(1.5))) END)
 
 (23) CometTakeOrderedAndProject
 Input [4]: [w_warehouse_name#7, i_item_id#9, inv_before#15, inv_after#16]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark3_5/q34/explain.txt

@@ -110,7 +110,7 @@ ReadSchema: struct<hd_demo_sk:int,hd_buy_potential:string,hd_dep_count:int,hd_ve
 
 (16) CometFilter
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]
-Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN ((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)) > 1.2) END) AND isnotnull(hd_demo_sk#12))
+Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)))) > knownfloatingpointnormalized(normalizenanandzero(1.2))) END) AND isnotnull(hd_demo_sk#12))
 
 (17) CometProject
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark3_5/q39a/explain.txt

@@ -152,11 +152,11 @@ Functions [2]: [stddev_samp(cast(inv_quantity_on_hand#3 as double)), avg(inv_qua
 
 (22) CometFilter
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#17, mean#18]
-Condition : CASE WHEN (mean#18 = 0.0) THEN false ELSE ((stdev#17 / mean#18) > 1.0) END
+Condition : CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#17 / mean#18))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END
 
 (23) CometProject
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#17, mean#18]
-Arguments: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, cov#19], [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, CASE WHEN (mean#18 = 0.0) THEN null ELSE (stdev#17 / mean#18) END AS cov#19]
+Arguments: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, cov#19], [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#17 / mean#18) END AS cov#19]
 
 (24) Scan parquet spark_catalog.default.inventory
 Output [4]: [inv_item_sk#20, inv_warehouse_sk#21, inv_quantity_on_hand#22, inv_date_sk#23]
@@ -238,11 +238,11 @@ Functions [2]: [stddev_samp(cast(inv_quantity_on_hand#22 as double)), avg(inv_qu
 
 (41) CometFilter
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, stdev#17, mean#18]
-Condition : CASE WHEN (mean#18 = 0.0) THEN false ELSE ((stdev#17 / mean#18) > 1.0) END
+Condition : CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#17 / mean#18))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END
 
 (42) CometProject
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, stdev#17, mean#18]
-Arguments: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37], [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#18 AS mean#36, CASE WHEN (mean#18 = 0.0) THEN null ELSE (stdev#17 / mean#18) END AS cov#37]
+Arguments: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37], [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#18 AS mean#36, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#17 / mean#18) END AS cov#37]
 
 (43) CometBroadcastExchange
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark3_5/q39b/explain.txt

@@ -152,11 +152,11 @@ Functions [2]: [stddev_samp(cast(inv_quantity_on_hand#3 as double)), avg(inv_qua
 
 (22) CometFilter
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#17, mean#18]
-Condition : (CASE WHEN (mean#18 = 0.0) THEN false ELSE ((stdev#17 / mean#18) > 1.0) END AND CASE WHEN (mean#18 = 0.0) THEN false ELSE ((stdev#17 / mean#18) > 1.5) END)
+Condition : (CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#17 / mean#18))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END AND CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#17 / mean#18))) > knownfloatingpointnormalized(normalizenanandzero(1.5))) END)
 
 (23) CometProject
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#17, mean#18]
-Arguments: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, cov#19], [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, CASE WHEN (mean#18 = 0.0) THEN null ELSE (stdev#17 / mean#18) END AS cov#19]
+Arguments: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, cov#19], [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#18, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#17 / mean#18) END AS cov#19]
 
 (24) Scan parquet spark_catalog.default.inventory
 Output [4]: [inv_item_sk#20, inv_warehouse_sk#21, inv_quantity_on_hand#22, inv_date_sk#23]
@@ -238,11 +238,11 @@ Functions [2]: [stddev_samp(cast(inv_quantity_on_hand#22 as double)), avg(inv_qu
 
 (41) CometFilter
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, stdev#17, mean#18]
-Condition : CASE WHEN (mean#18 = 0.0) THEN false ELSE ((stdev#17 / mean#18) > 1.0) END
+Condition : CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#17 / mean#18))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END
 
 (42) CometProject
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, stdev#17, mean#18]
-Arguments: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37], [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#18 AS mean#36, CASE WHEN (mean#18 = 0.0) THEN null ELSE (stdev#17 / mean#18) END AS cov#37]
+Arguments: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37], [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#18 AS mean#36, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#18)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#17 / mean#18) END AS cov#37]
 
 (43) CometBroadcastExchange
 Input [5]: [w_warehouse_sk#26, i_item_sk#25, d_moy#30, mean#36, cov#37]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark3_5/q73/explain.txt

@@ -110,7 +110,7 @@ ReadSchema: struct<hd_demo_sk:int,hd_buy_potential:string,hd_dep_count:int,hd_ve
 
 (16) CometFilter
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]
-Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN ((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)) > 1.0) END) AND isnotnull(hd_demo_sk#12))
+Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END) AND isnotnull(hd_demo_sk#12))
 
 (17) CometProject
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark4_0/q21/explain.txt

@@ -136,7 +136,7 @@ Results [4]: [w_warehouse_name#7, i_item_id#9, sum(CASE WHEN (d_date#12 < 2000-0
 
 (23) Filter [codegen id : 2]
 Input [4]: [w_warehouse_name#7, i_item_id#9, inv_before#19, inv_after#20]
-Condition : (CASE WHEN (inv_before#19 > 0) THEN ((cast(inv_after#20 as double) / cast(inv_before#19 as double)) >= 0.666667) END AND CASE WHEN (inv_before#19 > 0) THEN ((cast(inv_after#20 as double) / cast(inv_before#19 as double)) <= 1.5) END)
+Condition : (CASE WHEN (inv_before#19 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(inv_after#20 as double) / cast(inv_before#19 as double)))) >= knownfloatingpointnormalized(normalizenanandzero(0.666667))) END AND CASE WHEN (inv_before#19 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(inv_after#20 as double) / cast(inv_before#19 as double)))) <= knownfloatingpointnormalized(normalizenanandzero(1.5))) END)
 
 (24) TakeOrderedAndProject
 Input [4]: [w_warehouse_name#7, i_item_id#9, inv_before#19, inv_after#20]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark4_0/q34/explain.txt

@@ -110,7 +110,7 @@ ReadSchema: struct<hd_demo_sk:int,hd_buy_potential:string,hd_dep_count:int,hd_ve
 
 (16) CometFilter
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]
-Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN ((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)) > 1.2) END) AND isnotnull(hd_demo_sk#12))
+Condition : ((((isnotnull(hd_vehicle_count#15) AND ((hd_buy_potential#13 = >10000         ) OR (hd_buy_potential#13 = unknown        ))) AND (hd_vehicle_count#15 > 0)) AND CASE WHEN (hd_vehicle_count#15 > 0) THEN (knownfloatingpointnormalized(normalizenanandzero((cast(hd_dep_count#14 as double) / cast(hd_vehicle_count#15 as double)))) > knownfloatingpointnormalized(normalizenanandzero(1.2))) END) AND isnotnull(hd_demo_sk#12))
 
 (17) CometProject
 Input [4]: [hd_demo_sk#12, hd_buy_potential#13, hd_dep_count#14, hd_vehicle_count#15]

spark/src/test/resources/tpcds-plan-stability/approved-plans-v1_4-spark4_0/q39a/explain.txt

@@ -161,10 +161,10 @@ Results [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stddev_samp(cast(inv_quan
 
 (23) Filter [codegen id : 4]
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#24, mean#25]
-Condition : CASE WHEN (mean#25 = 0.0) THEN false ELSE ((stdev#24 / mean#25) > 1.0) END
+Condition : CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#25)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#24 / mean#25))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END
 
 (24) Project [codegen id : 4]
-Output [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#25, CASE WHEN (mean#25 = 0.0) THEN null ELSE (stdev#24 / mean#25) END AS cov#26]
+Output [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, mean#25, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#25)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#24 / mean#25) END AS cov#26]
 Input [5]: [w_warehouse_sk#7, i_item_sk#6, d_moy#11, stdev#24, mean#25]
 
 (25) Scan parquet spark_catalog.default.inventory
@@ -254,10 +254,10 @@ Results [5]: [w_warehouse_sk#33, i_item_sk#32, d_moy#37, stddev_samp(cast(inv_qu
 
 (43) Filter [codegen id : 3]
 Input [5]: [w_warehouse_sk#33, i_item_sk#32, d_moy#37, stdev#48, mean#49]
-Condition : CASE WHEN (mean#49 = 0.0) THEN false ELSE ((stdev#48 / mean#49) > 1.0) END
+Condition : CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#49)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN false ELSE (knownfloatingpointnormalized(normalizenanandzero((stdev#48 / mean#49))) > knownfloatingpointnormalized(normalizenanandzero(1.0))) END
 
 (44) Project [codegen id : 3]
-Output [5]: [w_warehouse_sk#33, i_item_sk#32, d_moy#37, mean#49, CASE WHEN (mean#49 = 0.0) THEN null ELSE (stdev#48 / mean#49) END AS cov#50]
+Output [5]: [w_warehouse_sk#33, i_item_sk#32, d_moy#37, mean#49, CASE WHEN (knownfloatingpointnormalized(normalizenanandzero(mean#49)) = knownfloatingpointnormalized(normalizenanandzero(0.0))) THEN null ELSE (stdev#48 / mean#49) END AS cov#50]
 Input [5]: [w_warehouse_sk#33, i_item_sk#32, d_moy#37, stdev#48, mean#49]
 
 (45) BroadcastExchange