diff --git a/core/src/execution/datafusion/planner.rs b/core/src/execution/datafusion/planner.rs
index 9afb0ebb88..ddaee5ff6e 100644
--- a/core/src/execution/datafusion/planner.rs
+++ b/core/src/execution/datafusion/planner.rs
@@ -20,6 +20,8 @@
 use std::{collections::HashMap, sync::Arc};
 
 use arrow_schema::{DataType, Field, Schema, TimeUnit};
+use datafusion::physical_plan::windows::BoundedWindowAggExec;
+use datafusion::physical_plan::InputOrderMode;
 use datafusion::{
     arrow::{compute::SortOptions, datatypes::SchemaRef},
     common::DataFusionError,
@@ -46,20 +48,23 @@ use datafusion::{
     },
     prelude::SessionContext,
 };
-use datafusion::physical_plan::InputOrderMode;
-use datafusion::physical_plan::windows::BoundedWindowAggExec;
 use datafusion_common::{
     tree_node::{Transformed, TransformedResult, TreeNode, TreeNodeRecursion, TreeNodeRewriter},
     JoinType as DFJoinType, ScalarValue,
 };
 use datafusion_expr::expr::find_df_window_func;
-use datafusion_expr::{ScalarUDF, WindowFrame, WindowFrameBound, WindowFrameUnits};
+use datafusion_expr::{
+    ScalarUDF, WindowFrame, WindowFrameBound, WindowFrameUnits, WindowFunctionDefinition,
+};
 use datafusion_physical_expr::window::WindowExpr;
 use datafusion_physical_expr_common::aggregate::create_aggregate_expr;
 use itertools::Itertools;
 use jni::objects::GlobalRef;
 use num::{BigInt, ToPrimitive};
 
+use crate::execution::spark_operator::lower_window_frame_bound::LowerFrameBoundStruct;
+use crate::execution::spark_operator::upper_window_frame_bound::UpperFrameBoundStruct;
+use crate::execution::spark_operator::WindowFrameType;
 use crate::{
     errors::ExpressionError,
     execution::{
@@ -100,9 +105,6 @@ use crate::{
         spark_partitioning::{partitioning::PartitioningStruct, Partitioning as SparkPartitioning},
     },
 };
-use crate::execution::spark_operator::lower_window_frame_bound::LowerFrameBoundStruct;
-use crate::execution::spark_operator::upper_window_frame_bound::UpperFrameBoundStruct;
-use crate::execution::spark_operator::WindowFrameType;
 
 use super::expressions::{abs::CometAbsFunc, EvalMode};
 
@@ -1384,7 +1386,7 @@ impl PhysicalPlanner {
                 Some(ExprStruct::ScalarFunc(f)) => {
                     window_func_name = f.func.clone();
                     window_func_args = f.args.clone();
-                },
+                }
                 other => {
                     return Err(ExecutionError::GeneralError(format!(
                         "{other:?} not supported for window function"
@@ -1396,8 +1398,9 @@ impl PhysicalPlanner {
             window_func_name = result.0;
             window_func_args = result.1;
         } else {
-            // Handle the case where neither func nor agg_func is set.
-            return Err(ExecutionError::GeneralError("Both func and agg_func are not set".to_string()));
+            return Err(ExecutionError::GeneralError(
+               "Both func and agg_func are not set".to_string(),
+            ));
         }
 
         let window_func = match find_df_window_func(&window_func_name) {
@@ -1484,15 +1487,16 @@ impl PhysicalPlanner {
             &input_schema,
             false, // TODO: Ignore nulls
         )
-            .map_err(|e| ExecutionError::DataFusionError(e.to_string()))
+        .map_err(|e| ExecutionError::DataFusionError(e.to_string()))
     }
 
-    fn process_agg_func(
-        agg_func: &AggExpr
-    ) -> Result<(String, Vec<Expr>), ExecutionError> {
+    fn process_agg_func(agg_func: &AggExpr) -> Result<(String, Vec<Expr>), ExecutionError> {
 
         fn optional_expr_to_vec(expr_option: &Option<Expr>) -> Vec<Expr> {
-            expr_option.as_ref().cloned().map_or_else(Vec::new, |e| vec![e])
+            expr_option
+                .as_ref()
+                .cloned()
+                .map_or_else(Vec::new, |e| vec![e])
         }
 
         fn int_to_stats_type(value: i32) -> Option<StatsType> {
@@ -1507,13 +1511,13 @@ impl PhysicalPlanner {
             Some(AggExprStruct::Count(expr)) => {
                 let args = &expr.children;
                 Ok(("count".to_string(), args.to_vec()))
-            },
+            }
             Some(AggExprStruct::Min(expr)) => {
                 Ok(("min".to_string(), optional_expr_to_vec(&expr.child)))
-            },
+            }
             Some(AggExprStruct::Max(expr)) => {
                 Ok(("max".to_string(), optional_expr_to_vec(&expr.child)))
-            },
+            }
             other => {
                 return Err(ExecutionError::GeneralError(format!(
                     "{other:?} not supported for window function"
diff --git a/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala b/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
index 45109d2e4c..29a9a1ff34 100644
--- a/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
+++ b/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
@@ -23,7 +23,7 @@ import scala.collection.JavaConverters._
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateMode, Average, BitAndAgg, BitOrAgg, BitXorAgg, Complete, Corr, Count, CovPopulation, CovSample, Final, First, Last, Max, Min, Partial, StddevPop, StddevSamp, Sum, VariancePop, VarianceSamp}
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, Average, BitAndAgg, BitOrAgg, BitXorAgg, Complete, Corr, Count, CovPopulation, CovSample, Final, First, Last, Max, Min, Partial, StddevPop, StddevSamp, Sum, VariancePop, VarianceSamp}
 import org.apache.spark.sql.catalyst.expressions.objects.StaticInvoke
 import org.apache.spark.sql.catalyst.optimizer.{BuildLeft, BuildRight, NormalizeNaNAndZero}
 import org.apache.spark.sql.catalyst.plans._