jorgecarleitao commented on a change in pull request #7984:
URL: https://github.com/apache/arrow/pull/7984#discussion_r472386086
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File path: rust/datafusion/src/execution/physical_plan/expressions.rs
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@@ -337,40 +336,86 @@ impl AggregateExpr for Avg {
}
}
-macro_rules! avg_accumulate {
+macro_rules! avg_accumulate_scalar {
($SELF:ident, $VALUE:expr, $ARRAY_TYPE:ident) => {{
- match ($SELF.sum, $SELF.count) {
- (Some(sum), Some(count)) => {
- $SELF.sum = Some(sum + $VALUE as f64);
+ // details here: https://stackoverflow.com/a/23493727/931303
+ match ($SELF.avg, $SELF.count) {
+ (Some(avg), Some(count)) => {
$SELF.count = Some(count + 1);
+ let online = (avg * (count as f64) + $VALUE as f64) / (count +
1) as f64;
+ $SELF.avg = Some(online);
}
_ => {
- $SELF.sum = Some($VALUE as f64);
$SELF.count = Some(1);
+ $SELF.avg = Some($VALUE as f64);
}
};
}};
}
+
+macro_rules! avg_accumulate_array {
+ ($SELF:ident, $ARRAY:expr, $ARRAY_TYPE:ident) => {{
+ // details here: https://stackoverflow.com/a/23493727/931303
+ let m = $ARRAY.len();
+ let sum =
compute::sum($ARRAY.as_any().downcast_ref::<$ARRAY_TYPE>().unwrap());
Review comment:
It is less likely to overflow over a single record batch than over a the
set of all record batches in a single partition.
I am taking a text book authoritative argument here, as I do not have
sufficient knowledge to take this debate to its details. I can see that spark
[uses
sum,count](https://github.com/apache/spark/blob/5264164a67df498b73facae207eda12ee133be7d/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java#L66),
so that is another argument (for what is worth).
No specific preference here. I implemented it as an example of an aggregate
UDF in one of my other PRs, and thought that it could be useful here.
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