fix: Average expression in Comet Final should handle all null inputs from partial Spark aggregation #261
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viirya
commented
Apr 11, 2024
| } else { | ||
| Ok(ScalarValue::Float64( | ||
| self.sum.map(|f| f / self.count as f64), | ||
| )) |
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Actually the GroupsAccumulator below has correctly implemented this behavior. Only AvgAccumulator has this issue.
…from partial Spark aggregation
huaxingao
approved these changes
Apr 12, 2024
sunchao
approved these changes
Apr 12, 2024
|
Merged. Thanks. |
himadripal
pushed a commit
to himadripal/datafusion-comet
that referenced
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Sep 7, 2024
…from partial Spark aggregation (apache#261)
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Which issue does this PR close?
Part of #260.
Rationale for this change
Comet Final aggregation could be used after Spark partial aggregation if columnar shuffle is enabled. While enabling columnar shuffle by default in #250, some Spark SQL tests with Average aggregation are failed on all null inputs.
It is because Comet Average expression relies on null buffers of input array to decide if the input are all null or not. But Spark Average expression simply relies on
countvalue. If all inputs are null values,countis 0.So in above case, Comet Average sees zero values on
sumandcountstates but it still computes the averagesum / countinstead of returning null. This is different to Spark behavior and we should fix it.What changes are included in this PR?
How are these changes tested?