liukun4515 commented on a change in pull request #1408:
URL: https://github.com/apache/arrow-datafusion/pull/1408#discussion_r769200447
##########
File path: datafusion/src/physical_plan/expressions/average.rs
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@@ -38,11 +38,18 @@ use super::{format_state_name, sum};
pub struct Avg {
name: String,
expr: Arc<dyn PhysicalExpr>,
+ data_type: DataType,
}
/// function return type of an average
pub fn avg_return_type(arg_type: &DataType) -> Result<DataType> {
match arg_type {
+ DataType::Decimal(precision, scale) => {
+ // the new precision and scale for return type of avg function
Review comment:
This is the intention for
https://github.com/apache/arrow-datafusion/issues/1418.
In the PG, we can create bigger precision for decimal than that in
datafusion.
The decimal in datafusion, whose behavior may be different from that in PG.
So we should discuss them.
Now for the promotion precision or scale, I just follow the spark behavior.
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