ozankabak commented on code in PR #14279:
URL: https://github.com/apache/datafusion/pull/14279#discussion_r1929486166
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -191,7 +201,18 @@ pub fn analyze(
shrink_boundaries(graph, target_boundaries,
target_expr_and_indices)
}
PropagationResult::Infeasible => {
- Ok(AnalysisContext::new(target_boundaries).with_selectivity(0.0))
+ // If the propagation result is infeasible, map target boundary
intervals to None
+ Ok(AnalysisContext::new(
Review Comment:
You seem to already own `target_boundaries`, no need to create a new
`ExprBoundaries` object. Just iterate over `target_boundaries` mutably and
assign `None` to the `interval` attribute.
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -179,7 +179,17 @@ pub fn analyze(
expr.as_any()
.downcast_ref::<Column>()
.filter(|expr_column| bound.column.eq(*expr_column))
- .map(|_| (*i, bound.interval.clone()))
+ .map(|_| {
+ (
+ *i,
+ match bound.interval.clone() {
Review Comment:
This function already returns a `Result`. You should return an error here
and not panic with `unreachable!`. Also doing the cloning *after* checking for
`None` is a slightly better style.
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -335,7 +355,9 @@ mod tests {
df_schema.as_ref(),
)
.unwrap();
- let actual = &analysis_result.boundaries[0].interval;
+ let Some(actual) = &analysis_result.boundaries[0].interval else {
+ panic!("Interval should be initialized for all columns");
Review Comment:
```suggestion
panic!("The analysis result should contain non-empty
intervals for all columns");
```
AFAICT this is not about initialization, you expect non-empty results.
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -82,7 +82,7 @@ impl AnalysisContext {
pub struct ExprBoundaries {
pub column: Column,
/// Minimum and maximum values this expression can have.
Review Comment:
```suggestion
/// Minimum and maximum values this expression can have. A `None` value
/// represents an infeasible expression.
```
##########
datafusion/physical-plan/src/filter.rs:
##########
@@ -421,7 +421,18 @@ fn collect_new_statistics(
..
},
)| {
- let (lower, upper) = interval.into_bounds();
+ let (lower, upper) = match interval {
+ Some(interval) => interval.into_bounds(),
+ // If the interval is None, we can say that there are no
rows
+ None => {
+ return ColumnStatistics {
+ null_count: Precision::Exact(0),
+ max_value:
Precision::Exact(ScalarValue::Int32(Some(0))),
+ min_value:
Precision::Exact(ScalarValue::Int32(Some(0))),
+ distinct_count: Precision::Exact(0),
+ }
+ }
+ };
Review Comment:
```suggestion
let Some(interval) = interval else {
// If the interval is `None`, we can say that there are
no rows:
return ColumnStatistics {
null_count: Precision::Exact(0),
max_value:
Precision::Exact(ScalarValue::Int32(Some(0))),
min_value:
Precision::Exact(ScalarValue::Int32(Some(0))),
distinct_count: Precision::Exact(0),
};
};
let (lower, upper) = interval.into_bounds();
```
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -235,16 +256,25 @@ fn shrink_boundaries(
fn calculate_selectivity(
target_boundaries: &[ExprBoundaries],
initial_boundaries: &[ExprBoundaries],
-) -> f64 {
+) -> Result<f64> {
// Since the intervals are assumed uniform and the values
// are not correlated, we need to multiply the selectivities
// of multiple columns to get the overall selectivity.
+ let mut acc: f64 = 1.0;
initial_boundaries
.iter()
.zip(target_boundaries.iter())
- .fold(1.0, |acc, (initial, target)| {
- acc * cardinality_ratio(&initial.interval, &target.interval)
- })
+ .for_each(|(initial, target)| {
Review Comment:
Similar to above -- let's return a `Result` instead of panicking and do any
checks before clones. Also, given that this is a good old for loop now, it will
increase readability to switch to a simple `for` statement instead of using a
closure within the `for_each` method.
##########
datafusion/physical-expr/src/analysis.rs:
##########
@@ -344,6 +366,41 @@ mod tests {
}
}
+ #[test]
+ fn test_analyze_empty_set_boundary_exprs() {
+ let schema = Arc::new(Schema::new(vec![make_field("a",
DataType::Int32)]));
+
+ let test_cases: Vec<Expr> = vec![
+ // a > 10 AND a < 10
+ col("a").gt(lit(10)).and(col("a").lt(lit(10))),
+ // a > 5 AND (a < 20 OR a > 20)
+ // a > 10 AND a < 20
+ // (a > 10 AND a < 20) AND (a > 20 AND a < 30)
+ col("a")
+ .gt(lit(10))
+ .and(col("a").lt(lit(20)))
+ .and(col("a").gt(lit(20)))
+ .and(col("a").lt(lit(30))),
+ ];
+
+ for expr in test_cases {
+ let boundaries =
ExprBoundaries::try_new_unbounded(&schema).unwrap();
+ let df_schema = DFSchema::try_from(Arc::clone(&schema)).unwrap();
+ let physical_expr =
+ create_physical_expr(&expr, &df_schema,
&ExecutionProps::new()).unwrap();
+ let analysis_result = analyze(
+ &physical_expr,
+ AnalysisContext::new(boundaries),
+ df_schema.as_ref(),
+ )
+ .unwrap();
+
+ analysis_result.boundaries.iter().for_each(|bound| {
Review Comment:
Good old for loop is preferable in cases like this
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