Kimahriman commented on code in PR #1073:
URL: https://github.com/apache/datafusion-comet/pull/1073#discussion_r1862550710
##########
native/spark-expr/src/list.rs:
##########
@@ -413,14 +426,297 @@ impl PartialEq<dyn Any> for GetArrayStructFields {
}
}
+#[derive(Debug, Hash)]
+pub struct ArrayInsert {
+ src_array_expr: Arc<dyn PhysicalExpr>,
+ pos_expr: Arc<dyn PhysicalExpr>,
+ item_expr: Arc<dyn PhysicalExpr>,
+ legacy_negative_index: bool,
+}
+
+impl ArrayInsert {
+ pub fn new(
+ src_array_expr: Arc<dyn PhysicalExpr>,
+ pos_expr: Arc<dyn PhysicalExpr>,
+ item_expr: Arc<dyn PhysicalExpr>,
+ legacy_negative_index: bool,
+ ) -> Self {
+ Self {
+ src_array_expr,
+ pos_expr,
+ item_expr,
+ legacy_negative_index,
+ }
+ }
+}
+
+impl PhysicalExpr for ArrayInsert {
+ fn as_any(&self) -> &dyn Any {
+ self
+ }
+
+ fn data_type(&self, input_schema: &Schema) -> DataFusionResult<DataType> {
+ match self.src_array_expr.data_type(input_schema)? {
+ DataType::List(field) => Ok(DataType::List(field)),
+ DataType::LargeList(field) => Ok(DataType::LargeList(field)),
+ data_type => Err(DataFusionError::Internal(format!(
+ "Unexpected data type in ArrayInsert: {:?}",
+ data_type
+ ))),
+ }
+ }
+
+ fn nullable(&self, input_schema: &Schema) -> DataFusionResult<bool> {
+ self.src_array_expr.nullable(input_schema)
+ }
+
+ fn evaluate(&self, batch: &RecordBatch) -> DataFusionResult<ColumnarValue>
{
+ let pos_value = self
+ .pos_expr
+ .evaluate(batch)?
+ .into_array(batch.num_rows())?;
+
+ // Spark supports only IntegerType (Int32):
+ //
https://github.com/apache/spark/blob/branch-3.5/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala#L4737
+ if !matches!(pos_value.data_type(), DataType::Int32) {
+ return Err(DataFusionError::Internal(format!(
+ "Unexpected index data type in ArrayInsert: {:?}, expected
type is Int32",
+ pos_value.data_type()
+ )));
+ }
+
+ // Check that src array is actually an array and get it's value type
+ let src_value = self
+ .src_array_expr
+ .evaluate(batch)?
+ .into_array(batch.num_rows())?;
+ let src_element_type = match src_value.data_type() {
+ DataType::List(field) => field.data_type(),
+ DataType::LargeList(field) => field.data_type(),
+ data_type => {
+ return Err(DataFusionError::Internal(format!(
+ "Unexpected src array type in ArrayInsert: {:?}",
+ data_type
+ )))
+ }
Review Comment:
The difference I think is that a `LargeList` can store more than
`Integer.MAX_VALUE` entries in _all rows in a single batch_, so if you have
multiple Spark rows all with the max num of rows supported, it wouldn't fit
into an Arrow `List` array. That would probably need to be supported elsewhere,
but it may be worth keeping the `LargeList` handling around in case that
scenario is supported. And other DataFusion expressions might return a
`LargeList` even if it doesn't come directly from Spark? Does the native
Parquet reader ever use a `LargeList`?
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