zhangfengcdt commented on code in PR #6928:
URL: https://github.com/apache/datafusion-comet/pull/6928#discussion_r4259986541
##########
native/spark-expr/src/agg_funcs/percentile.rs:
##########
@@ -285,46 +321,228 @@ impl GroupsAccumulator for
SparkPercentileGroupsAccumulator {
.map(|values| values.capacity() * size_of::<f64>())
.sum::<usize>()
+ self.group_values.capacity() * size_of::<Vec<f64>>()
+ + self
+ .group_partial_ends
+ .iter()
+ .map(|ends| ends.capacity() * size_of::<usize>())
+ .sum::<usize>()
+ + self.group_partial_ends.capacity() * size_of::<Vec<usize>>()
}
}
-fn spark_percentile(values: &mut [f64], percentile: f64) -> Option<f64> {
+/// `partial_ends` marks where each partial state merged into `values` ends.
Values past the last
+/// one came straight from input rows, as in Spark's `Complete` mode.
+fn spark_percentile(
+ values: &mut [f64],
+ partial_ends: &[usize],
+ percentile: f64,
+ float_input: bool,
+) -> Option<f64> {
let len = values.len();
if len == 0 {
return None;
}
if len == 1 {
return Some(values[0]);
}
+ // Read before the selection below reorders the values.
+ let zeros = SignedZeros::find(values, partial_ends, float_input);
Review Comment:
Done! Now, the ranks are computed first and the replay only runs upon
failures on a zero. A median or maximum away from zero can skip it.
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