diff --git a/native/core/src/parquet/parquet_exec.rs b/native/core/src/parquet/parquet_exec.rs
index e49bfa981c..79c7e06c63 100644
--- a/native/core/src/parquet/parquet_exec.rs
+++ b/native/core/src/parquet/parquet_exec.rs
@@ -84,11 +84,11 @@ pub(crate) fn init_datasource_exec(
 
     // Determine the schema to use for ParquetSource
     // // Use data_schema only if both data_schema and data_filters are set
-    // let base_schema = match (&data_schema, &data_filters) {
-    //     (Some(schema), Some(_)) => Arc::clone(schema),
-    //     _ => Arc::clone(&required_schema),
-    // };
-    let base_schema = required_schema;
+    let base_schema = match (&data_schema, &projection_vector) {
+        (Some(schema), Some(_)) => Arc::clone(schema),
+        _ => Arc::clone(&required_schema),
+    };
+    //let base_schema = required_schema;
     // dbg!(&base_schema);
     // dbg!(&data_schema);
     // dbg!(&data_filters);
diff --git a/native/spark-expr/src/conversion_funcs/cast.rs b/native/spark-expr/src/conversion_funcs/cast.rs
index b63610afd6..41f70fd2a1 100644
--- a/native/spark-expr/src/conversion_funcs/cast.rs
+++ b/native/spark-expr/src/conversion_funcs/cast.rs
@@ -906,10 +906,10 @@ pub fn spark_cast(
     data_type: &DataType,
     cast_options: &SparkCastOptions,
 ) -> DataFusionResult<ColumnarValue> {
-    let input_type = match &arg {
-        ColumnarValue::Array(array) => array.data_type().clone(),
-        ColumnarValue::Scalar(scalar) => scalar.data_type(),
-    };
+    // let input_type = match &arg {
+    //     ColumnarValue::Array(array) => array.data_type().clone(),
+    //     ColumnarValue::Scalar(scalar) => scalar.data_type(),
+    // };
 
     let result = match arg {
         ColumnarValue::Array(array) => {
@@ -927,10 +927,10 @@ pub fn spark_cast(
         }
     };
 
-    let result_type = match &result {
-        ColumnarValue::Array(array) => array.data_type().clone(),
-        ColumnarValue::Scalar(scalar) => scalar.data_type(),
-    };
+    // let result_type = match &result {
+    //     ColumnarValue::Array(array) => array.data_type().clone(),
+    //     ColumnarValue::Scalar(scalar) => scalar.data_type(),
+    // };
 
     // println!(
     //     "spark_cast: {} -> {} (requested: {})",
diff --git a/native/spark-expr/src/csv_funcs/to_csv.rs b/native/spark-expr/src/csv_funcs/to_csv.rs
index 5816f79993..f41cb7f918 100644
--- a/native/spark-expr/src/csv_funcs/to_csv.rs
+++ b/native/spark-expr/src/csv_funcs/to_csv.rs
@@ -115,8 +115,8 @@ impl PhysicalExpr for ToCsv {
         )))
     }
 
-    fn fmt_sql(&self, _: &mut Formatter<'_>) -> std::fmt::Result {
-        unimplemented!()
+    fn fmt_sql(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+        Display::fmt(self, f)
     }
 }
 
diff --git a/native/spark-expr/src/unbound.rs b/native/spark-expr/src/unbound.rs
index de9e6e2e3e..cf0adafa91 100644
--- a/native/spark-expr/src/unbound.rs
+++ b/native/spark-expr/src/unbound.rs
@@ -20,7 +20,7 @@ use arrow::datatypes::{DataType, Schema};
 use datafusion::common::{internal_err, Result};
 use datafusion::physical_expr::PhysicalExpr;
 use datafusion::physical_plan::ColumnarValue;
-use std::fmt::Formatter;
+use std::fmt::{Display, Formatter};
 use std::{hash::Hash, sync::Arc};
 
 /// This is similar to `UnKnownColumn` in DataFusion, but it has data type.
@@ -64,8 +64,8 @@ impl PhysicalExpr for UnboundColumn {
         self
     }
 
-    fn fmt_sql(&self, _: &mut Formatter<'_>) -> std::fmt::Result {
-        unimplemented!()
+    fn fmt_sql(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+        Display::fmt(self, f)
     }
 
     /// Get the data type of this expression, given the schema of the input