fix

core/src/execution/datafusion/expressions/stddev.rs

@@ -31,6 +31,10 @@ use crate::execution::datafusion::expressions::utils::down_cast_any_ref;
 use crate::execution::datafusion::expressions::variance::VarianceAccumulator;
 
 /// STDDEV and STDDEV_SAMP (standard deviation) aggregate expression
+/// The implementation mostly is the same as the DataFusion's implementation. The reason
+/// we have our own implementation is that DataFusion has UInt64 for state_field `count`,
+/// while Spark has Double for count. Also we have added `null_on_divide_by_zero`
+/// to be consistent with Spark's implementation.
 #[derive(Debug)]
 pub struct Stddev {
     name: String,
@@ -112,7 +116,10 @@ impl PartialEq<dyn Any> for Stddev {
     fn eq(&self, other: &dyn Any) -> bool {
         down_cast_any_ref(other)
             .downcast_ref::<Self>()
-            .map(|x| self.name == x.name && self.expr.eq(&x.expr))
+            .map(|x|
+                self.name == x.name
+                && self.expr.eq(&x.expr)
+                && self.null_on_divide_by_zero == x.null_on_divide_by_zero)
             .unwrap_or(false)
     }
 }

docs/source/user-guide/expressions.md

@@ -107,3 +107,5 @@ The following Spark expressions are currently available:
   - CovSample
   - VariancePop
   - VarianceSamp
+  - StddevPop
+  - StddevSamp

spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala

@@ -521,6 +521,7 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde {
               .setStddev(stdBuilder)
               .build())
         } else {
+          withInfo(aggExpr, child)
           None
         }
       case std @ StddevPop(child, nullOnDivideByZero) =>
@@ -540,6 +541,7 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde {
               .setStddev(stdBuilder)
               .build())
         } else {
+          withInfo(aggExpr, child)
           None
         }
       case fn =>