MichaelScofield commented on code in PR #4553:
URL: https://github.com/apache/arrow-datafusion/pull/4553#discussion_r1045378304
##########
datafusion/sql/src/planner.rs:
##########
@@ -2408,6 +2406,21 @@ impl<'a, S: ContextProvider> SqlToRel<'a, S> {
}
}
+ fn find_window_func(&self, name: &str) -> Result<WindowFunction> {
+ window_function::find_df_window_func(name)
Review Comment:
Yes, I think built-in functions are always take higher precedence to UDAFs.
##########
datafusion/core/src/physical_plan/windows/mod.rs:
##########
@@ -180,6 +188,81 @@ mod tests {
Ok((csv, schema))
}
+ #[tokio::test]
+ async fn window_function_with_udaf() -> Result<()> {
+ #[derive(Debug)]
+ struct MyCount(i64);
+
+ impl Accumulator for MyCount {
+ fn state(&self) -> Result<Vec<AggregateState>> {
+ Ok(vec![AggregateState::Scalar(ScalarValue::Int64(Some(
+ self.0,
+ )))])
+ }
+
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let array = &values[0];
+ self.0 += (array.len() - array.data().null_count()) as i64;
+ Ok(())
+ }
+
+ fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+ let counts = downcast_value!(states[0], Int64Array);
+ if let Some(c) = &arrow::compute::sum(counts) {
+ self.0 += *c;
+ }
+ Ok(())
+ }
+
+ fn evaluate(&self) -> Result<ScalarValue> {
+ Ok(ScalarValue::Int64(Some(self.0)))
+ }
+
+ fn size(&self) -> usize {
+ std::mem::size_of_val(self)
+ }
+ }
+
+ let my_count = create_udaf(
+ "my_count",
+ DataType::Int64,
+ Arc::new(DataType::Int64),
+ Volatility::Immutable,
+ Arc::new(|_| Ok(Box::new(MyCount(0)))),
+ Arc::new(vec![DataType::Int64]),
+ );
+
+ let session_ctx = SessionContext::new();
+ let task_ctx = session_ctx.task_ctx();
+ let (input, schema) = create_test_schema(1)?;
+
+ let window_exec = Arc::new(WindowAggExec::try_new(
Review Comment:
thx!
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