ozankabak commented on code in PR #14699:
URL: https://github.com/apache/datafusion/pull/14699#discussion_r1966455316
##########
datafusion/physical-expr-common/src/physical_expr.rs:
##########
@@ -144,6 +153,111 @@ pub trait PhysicalExpr: Send + Sync + Display + Debug +
DynEq + DynHash {
Ok(Some(vec![]))
}
+ /// Computes the output statistics for the expression, given the input
+ /// statistics.
+ ///
+ /// # Parameters
+ ///
+ /// * `children` are the statistics for the children (inputs) of this
+ /// expression.
+ ///
+ /// # Returns
+ ///
+ /// A `Result` containing the output statistics for the expression in
+ /// case of success, or an error object in case of failure.
+ ///
+ /// Expressions (should) implement this function and utilize the
independence
+ /// assumption, match on children distribution types and compute the output
+ /// statistics accordingly. The default implementation simply creates an
+ /// unknown output distribution by combining input ranges. This logic loses
+ /// distribution information, but is a safe default.
+ fn evaluate_statistics(&self, children: &[&StatisticsV2]) ->
Result<StatisticsV2> {
+ let children_ranges = children
+ .iter()
+ .map(|c| c.range())
+ .collect::<Result<Vec<_>>>()?;
+ let children_ranges_refs = children_ranges.iter().collect::<Vec<_>>();
+ let output_interval =
self.evaluate_bounds(children_ranges_refs.as_slice())?;
+ let dt = output_interval.data_type();
+ if dt.eq(&DataType::Boolean) {
+ let p = if output_interval.eq(&Interval::CERTAINLY_TRUE) {
+ ScalarValue::new_one(&dt)
+ } else if output_interval.eq(&Interval::CERTAINLY_FALSE) {
+ ScalarValue::new_zero(&dt)
+ } else {
+ ScalarValue::try_from(&dt)
+ }?;
+ StatisticsV2::new_bernoulli(p)
Review Comment:
Because there is no other choice :) The only applicable distribution in case
of a boolean variable is the Bernoulli distribution. Bernoulli distribution is
just the stats term for a boolean variable with a parameter for "probability of
being true".
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