fix: [df52] schema pruning crash on complex nested types

native/core/src/parquet/parquet_exec.rs

@@ -80,18 +80,40 @@ pub(crate) fn init_datasource_exec(
         encryption_enabled,
     );
 
-    // dbg!(&required_schema, &data_schema);
-
-    // 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, &projection_vector) {
-        (Some(schema), Some(_)) => Arc::clone(schema),
-        _ => Arc::clone(&required_schema),
+    // Determine the schema and projection to use for ParquetSource.
+    // When data_schema is provided, use it as the base schema so DataFusion knows the full
+    // file schema. Compute a projection vector to select only the required columns.
+    let (base_schema, projection) = match (&data_schema, &projection_vector) {
+        (Some(schema), Some(proj)) => (Arc::clone(schema), Some(proj.clone())),
+        (Some(schema), None) => {
+            // Compute projection: map required_schema field names to data_schema indices.
+            // This is needed for schema pruning when the data_schema has more columns than
+            // the required_schema.
+            let projection: Vec<usize> = required_schema
+                .fields()
+                .iter()
+                .filter_map(|req_field| {
+                    schema.fields().iter().position(|data_field| {
+                        if case_sensitive {
+                            data_field.name() == req_field.name()
+                        } else {
+                            data_field.name().to_lowercase() == req_field.name().to_lowercase()
+                        }
+                    })
+                })
+                .collect();
+            // Only use data_schema + projection when all required fields were found by name.
+            // When some fields can't be matched (e.g., Parquet field ID mapping where names
+            // differ between required and data schemas), fall back to using required_schema
+            // directly with no projection.
+            if projection.len() == required_schema.fields().len() {
+                (Arc::clone(schema), Some(projection))
+            } else {
+                (Arc::clone(&required_schema), None)
+            }
+        }
+        _ => (Arc::clone(&required_schema), None),
     };
-    //let base_schema = required_schema;
-    // dbg!(&base_schema);
-    // dbg!(&data_schema);
-    // dbg!(&data_filters);
     let partition_fields: Vec<_> = partition_schema
         .iter()
         .flat_map(|s| s.fields().iter())
@@ -100,13 +122,9 @@ pub(crate) fn init_datasource_exec(
     let table_schema =
         TableSchema::from_file_schema(base_schema).with_table_partition_cols(partition_fields);
 
-    // dbg!(&table_schema);
-
     let mut parquet_source =
         ParquetSource::new(table_schema).with_table_parquet_options(table_parquet_options);
 
-    // dbg!(&parquet_source);
-
     // Create a conjunctive form of the vector because ParquetExecBuilder takes
     // a single expression
     if let Some(data_filters) = data_filters {
@@ -146,9 +164,9 @@ pub(crate) fn init_datasource_exec(
         .with_file_groups(file_groups)
         .with_expr_adapter(Some(expr_adapter_factory));
 
-    if let Some(projection_vector) = projection_vector {
+    if let Some(projection) = projection {
         file_scan_config_builder =
-            file_scan_config_builder.with_projection_indices(Some(projection_vector))?;
+            file_scan_config_builder.with_projection_indices(Some(projection))?;
     }
 
     let file_scan_config = file_scan_config_builder.build();

native/core/src/parquet/schema_adapter.rs

@@ -262,9 +262,22 @@ impl SparkPhysicalExprAdapter {
         expr.transform(|e| {
             if let Some(column) = e.as_any().downcast_ref::<Column>() {
                 let col_idx = column.index();
+                let col_name = column.name();
 
                 let logical_field = self.logical_file_schema.fields().get(col_idx);
-                let physical_field = self.physical_file_schema.fields().get(col_idx);
+                // Look up physical field by name instead of index for correctness
+                // when logical and physical schemas have different column orderings
+                let physical_field = if self.parquet_options.case_sensitive {
+                    self.physical_file_schema
+                        .fields()
+                        .iter()
+                        .find(|f| f.name() == col_name)
+                } else {
+                    self.physical_file_schema
+                        .fields()
+                        .iter()
+                        .find(|f| f.name().to_lowercase() == col_name.to_lowercase())
+                };
 
                 if let (Some(logical_field), Some(physical_field)) = (logical_field, physical_field)
                 {