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new e54eb42388 Fix predicate_rows_pruned & predicate_rows_matched metrics
(#18980)
e54eb42388 is described below
commit e54eb423881f2baa3800cba3338ca6f9b5a0d300
Author: xudong.w <[email protected]>
AuthorDate: Mon Dec 1 04:49:23 2025 +0800
Fix predicate_rows_pruned & predicate_rows_matched metrics (#18980)
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- Closes #.
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---
.../core/src/datasource/physical_plan/parquet.rs | 94 ++++++++++++++++++++++
datafusion/datasource-parquet/src/row_filter.rs | 25 +++++-
2 files changed, 116 insertions(+), 3 deletions(-)
diff --git a/datafusion/core/src/datasource/physical_plan/parquet.rs
b/datafusion/core/src/datasource/physical_plan/parquet.rs
index 90953e3f5d..4d7c4a8788 100644
--- a/datafusion/core/src/datasource/physical_plan/parquet.rs
+++ b/datafusion/core/src/datasource/physical_plan/parquet.rs
@@ -1874,6 +1874,100 @@ mod tests {
assert_contains!(&explain, "projection=[c1]");
}
+ #[tokio::test]
+ async fn parquet_exec_metrics_with_multiple_predicates() {
+ // Test that metrics are correctly calculated when multiple predicates
+ // are pushed down (connected with AND). This ensures we don't
double-count
+ // rows when multiple predicates filter the data sequentially.
+
+ // Create a batch with two columns: c1 (string) and c2 (int32)
+ // Total: 10 rows
+ let c1: ArrayRef = Arc::new(StringArray::from(vec![
+ Some("foo"), // 0 - passes c1 filter, fails c2 filter (5 <= 10)
+ Some("bar"), // 1 - fails c1 filter
+ Some("bar"), // 2 - fails c1 filter
+ Some("baz"), // 3 - passes both filters (20 > 10)
+ Some("foo"), // 4 - passes both filters (12 > 10)
+ Some("bar"), // 5 - fails c1 filter
+ Some("baz"), // 6 - passes both filters (25 > 10)
+ Some("foo"), // 7 - passes c1 filter, fails c2 filter (7 <= 10)
+ Some("bar"), // 8 - fails c1 filter
+ Some("qux"), // 9 - passes both filters (30 > 10)
+ ]));
+
+ let c2: ArrayRef = Arc::new(Int32Array::from(vec![
+ Some(5),
+ Some(15),
+ Some(8),
+ Some(20),
+ Some(12),
+ Some(9),
+ Some(25),
+ Some(7),
+ Some(18),
+ Some(30),
+ ]));
+
+ let batch = create_batch(vec![("c1", c1), ("c2", c2)]);
+
+ // Create filter: c1 != 'bar' AND c2 > 10
+ //
+ // First predicate (c1 != 'bar'):
+ // - Rows passing: 0, 3, 4, 6, 7, 9 (6 rows)
+ // - Rows pruned: 1, 2, 5, 8 (4 rows)
+ //
+ // Second predicate (c2 > 10) on remaining 6 rows:
+ // - Rows passing: 3, 4, 6, 9 (4 rows with c2 = 20, 12, 25, 30)
+ // - Rows pruned: 0, 7 (2 rows with c2 = 5, 7)
+ //
+ // Expected final metrics:
+ // - pushdown_rows_matched: 4 (final result)
+ // - pushdown_rows_pruned: 4 + 2 = 6 (cumulative)
+ // - Total: 4 + 6 = 10
+
+ let filter = col("c1").not_eq(lit("bar")).and(col("c2").gt(lit(10)));
+
+ let rt = RoundTrip::new()
+ .with_predicate(filter)
+ .with_pushdown_predicate()
+ .round_trip(vec![batch])
+ .await;
+
+ let metrics = rt.parquet_exec.metrics().unwrap();
+
+ // Verify the result rows
+ assert_snapshot!(batches_to_string(&rt.batches.unwrap()),@r###"
+ +-----+----+
+ | c1 | c2 |
+ +-----+----+
+ | baz | 20 |
+ | foo | 12 |
+ | baz | 25 |
+ | qux | 30 |
+ +-----+----+
+ "###);
+
+ // Verify metrics - this is the key test
+ let pushdown_rows_matched = get_value(&metrics,
"pushdown_rows_matched");
+ let pushdown_rows_pruned = get_value(&metrics, "pushdown_rows_pruned");
+
+ assert_eq!(
+ pushdown_rows_matched, 4,
+ "Expected 4 rows to pass both predicates"
+ );
+ assert_eq!(
+ pushdown_rows_pruned, 6,
+ "Expected 6 rows to be pruned (4 by first predicate + 2 by second
predicate)"
+ );
+
+ // The sum should equal the total number of rows
+ assert_eq!(
+ pushdown_rows_matched + pushdown_rows_pruned,
+ 10,
+ "matched + pruned should equal total rows"
+ );
+ }
+
#[tokio::test]
async fn parquet_exec_has_no_pruning_predicate_if_can_not_prune() {
// batch1: c1(string)
diff --git a/datafusion/datasource-parquet/src/row_filter.rs
b/datafusion/datasource-parquet/src/row_filter.rs
index 660b32f486..de9fe181f8 100644
--- a/datafusion/datasource-parquet/src/row_filter.rs
+++ b/datafusion/datasource-parquet/src/row_filter.rs
@@ -470,14 +470,33 @@ pub fn build_row_filter(
});
}
+ // To avoid double-counting metrics when multiple predicates are used:
+ // - All predicates should count rows_pruned (cumulative pruned rows)
+ // - Only the last predicate should count rows_matched (final result)
+ // This ensures: rows_matched + rows_pruned = total rows processed
+ let total_candidates = candidates.len();
+
candidates
.into_iter()
- .map(|candidate| {
+ .enumerate()
+ .map(|(idx, candidate)| {
+ let is_last = idx == total_candidates - 1;
+
+ // All predicates share the pruned counter (cumulative)
+ let predicate_rows_pruned = rows_pruned.clone();
+
+ // Only the last predicate tracks matched rows (final result)
+ let predicate_rows_matched = if is_last {
+ rows_matched.clone()
+ } else {
+ metrics::Count::new()
+ };
+
DatafusionArrowPredicate::try_new(
candidate,
metadata,
- rows_pruned.clone(),
- rows_matched.clone(),
+ predicate_rows_pruned,
+ predicate_rows_matched,
time.clone(),
)
.map(|pred| Box::new(pred) as _)
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