andygrove commented on code in PR #461:
URL: https://github.com/apache/datafusion-comet/pull/461#discussion_r1613955491
##########
core/src/execution/datafusion/expressions/cast.rs:
##########
@@ -622,14 +590,89 @@ impl Cast {
self.eval_mode,
from_type,
to_type,
- )?
+ )
+ }
+ _ if Self::is_datafusion_spark_compatible(from_type, to_type) => {
+ // use DataFusion cast only when we know that it is compatible
with Spark
+ Ok(cast_with_options(&array, to_type, &CAST_OPTIONS)?)
}
_ => {
- // when we have no Spark-specific casting we delegate to
DataFusion
- cast_with_options(&array, to_type, &CAST_OPTIONS)?
+ // we should never reach this code because the Scala code
should be checking
+ // for supported cast operations and falling back to Spark for
anything that
+ // is not yet supported
+ Err(CometError::Internal(format!(
+ "Native cast invoked for unsupported cast from
{from_type:?} to {to_type:?}"
+ )))
}
};
- Ok(spark_cast(cast_result, from_type, to_type))
+ Ok(spark_cast(cast_result?, from_type, to_type))
+ }
+
+ /// Determines if DataFusion supports the given cast in a way that is
+ /// compatible with Spark
+ fn is_datafusion_spark_compatible(from_type: &DataType, to_type:
&DataType) -> bool {
+ if from_type == to_type {
+ return true;
+ }
+ match from_type {
+ DataType::Boolean => matches!(
+ to_type,
+ DataType::Int8
+ | DataType::Int16
+ | DataType::Int32
+ | DataType::Int64
+ | DataType::Float32
+ | DataType::Float64
+ | DataType::Utf8
Review Comment:
The current code says that datafusion is compatible with Spark for all int
types -> decima;l:
```
DataType::Int8 | DataType::Int16 | DataType::Int32 | DataType::Int64 =>
matches!(
to_type,
DataType::Boolean
...
| DataType::Decimal128(_, _)
```
However, this is actually not correct since DataFusion does not have
overflow checks for int32 and int64 -> decimal and is not compatible with
Spark. I will look at removing those.
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