rich7420 commented on code in PR #6180:
URL: https://github.com/apache/datafusion-comet/pull/6180#discussion_r4240323303
##########
spark/src/test/scala/org/apache/comet/exec/CometExecSuite.scala:
##########
@@ -2549,6 +2549,37 @@ class CometExecSuite extends CometTestBase {
}
}
+ test("filter output projection preserves rows, aliases and metrics") {
Review Comment:
Moved the projection result regression to
`spark/src/test/resources/sql-tests/operators/filter_projection.sql` in #6396,
which is now merged. It covers count-only output, aliases/order/duplicates,
full projections, empty results and a rejected row that must not reach an ANSI
cast. The Scala test retains the native output-row metrics checks, which the
SQL fixture cannot assert. These generic projection changes are no longer in
this PR’s diff.
##########
native/core/src/execution/operators/projection.rs:
##########
@@ -57,12 +60,28 @@ impl OperatorBuilder for ProjectionBuilder {
.map(|(idx, expr)| {
planner
.create_expr(expr, child.schema())
- .map(|r| (r, format!("col_{idx}")))
+ .map(|r| ProjectionExpr::new(r, format!("col_{idx}")))
})
.collect();
+ let mut exprs = ProjectionExprs::from(exprs?);
Review Comment:
Split out as #6396 and merged. This branch now takes the projection
implementation from main, so this PR’s diff is limited to AtLeastNNonNulls and
its configuration, tests, benchmarks and documentation. The shared CASE/IF
implementation (#6350) and independent IF tests (#6397) are also merged.
##########
native/spark-expr/src/predicate_funcs/at_least_n_non_nulls.rs:
##########
@@ -0,0 +1,553 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use std::fmt::{Display, Formatter};
+use std::sync::Arc;
+
+use arrow::array::{Array, ArrayRef, AsArray, BooleanArray, RecordBatch};
+use arrow::buffer::{BooleanBuffer, Buffer};
+use arrow::compute::cast;
+use arrow::datatypes::{DataType, Float32Type, Float64Type, Schema};
+use datafusion::common::{Result, ScalarValue};
+use datafusion::physical_expr::expressions::{Column, Literal};
+use datafusion::physical_expr::PhysicalExpr;
+use datafusion::physical_plan::ColumnarValue;
+
+/// Spark's per-row count of non-null, non-NaN children, stopping once `n` is
reached.
+#[derive(Debug, PartialEq, Eq, Hash)]
+pub struct AtLeastNNonNulls {
+ n: i32,
+ children: Vec<Arc<dyn PhysicalExpr>>,
+}
+
+impl AtLeastNNonNulls {
+ pub fn new(n: i32, children: Vec<Arc<dyn PhysicalExpr>>) -> Self {
+ Self { n, children }
+ }
+
+ // Sub-word batches do not amortize the bitmap counter's bookkeeping. Keep
+ // the row counter for these batches, including single-row scalar inputs.
+ fn evaluate_small_batch(&self, batch: &RecordBatch) ->
Result<ColumnarValue> {
+ let mut counts = vec![0; batch.num_rows()];
+ let mut remaining = batch.num_rows();
+ for child in &self.children {
+ if remaining == 0 {
+ break;
+ }
+ // Column/literal reads cannot raise per-row errors or have side
effects. Avoid
+ // filtering the input batch for the common DataFrame.na.drop
attribute inputs.
+ let value =
+ if remaining == batch.num_rows() || child.is::<Column>() ||
child.is::<Literal>() {
+ child.evaluate(batch)?
+ } else {
+ let selection = BooleanArray::new(
+ BooleanBuffer::collect_bool(counts.len(), |i|
counts[i] < self.n),
+ None,
+ );
+ child.evaluate_selection(batch, &selection)?
+ };
+ let scalar = matches!(value, ColumnarValue::Scalar(_));
+ let array = value.into_array(if scalar { 1 } else {
batch.num_rows() })?;
+ let valid = valid_values(&array)?;
+ if scalar {
+ if valid.value(0) {
+ for count in &mut counts {
+ if *count < self.n {
+ *count += 1;
+ remaining -= usize::from(*count == self.n);
+ }
+ }
+ }
+ } else {
+ for row in valid.set_indices() {
+ if counts[row] < self.n {
+ counts[row] += 1;
+ remaining -= usize::from(counts[row] == self.n);
+ }
+ }
+ }
+ }
+ Ok(ColumnarValue::Array(Arc::new(BooleanArray::new(
+ BooleanBuffer::collect_bool(counts.len(), |i| counts[i] >= self.n),
+ None,
+ ))))
+ }
+}
+
+impl Display for AtLeastNNonNulls {
+ fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+ write!(f, "atleastnnonnulls({}, {:?})", self.n, self.children)
+ }
+}
+
+impl PhysicalExpr for AtLeastNNonNulls {
+ fn fmt_sql(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+ Display::fmt(self, f)
+ }
+
+ fn data_type(&self, _input_schema: &Schema) -> Result<DataType> {
+ Ok(DataType::Boolean)
+ }
+
+ fn nullable(&self, _input_schema: &Schema) -> Result<bool> {
+ Ok(false)
+ }
+
+ fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
+ if self.n <= 0 {
+ return Ok(ColumnarValue::Scalar(ScalarValue::Boolean(Some(true))));
+ }
+ // The standard na.drop("all"/"any") cases reduce to bitmap OR/AND and
do not
+ // need a per-row counter. For AND, still evaluate every child: Spark
does not
+ // skip later expressions just because a row can no longer reach the
threshold.
+ if self.n == 1 || self.n as usize == self.children.len() {
+ let all = self.n != 1;
+ let mut result = if all {
+ BooleanBuffer::new_set(batch.num_rows())
+ } else {
+ BooleanBuffer::new_unset(batch.num_rows())
+ };
+ for child in &self.children {
+ if batch.num_rows() == 0 || (!all && result.count_set_bits()
== batch.num_rows()) {
+ break;
+ }
+ let value = if all || child.is::<Column>() ||
child.is::<Literal>() {
+ child.evaluate(batch)?
+ } else {
+ child.evaluate_selection(batch,
&BooleanArray::new(!&result, None))?
+ };
+ let valid =
valid_values(&value.into_array(batch.num_rows())?)?;
+ result = if all {
+ &result & &valid
+ } else {
+ &result | &valid
+ };
+ }
+ return Ok(ColumnarValue::Array(Arc::new(BooleanArray::new(
+ result, None,
+ ))));
+ }
+ if batch.num_rows() < 64 {
Review Comment:
64 was a one-word starting point for the general counting path: bitmap
counters process 64 rows per word, while small batches may not amortize their
bookkeeping. It is a heuristic, not a required or universal crossover. I made
it configurable as `spark.comet.exec.atLeastNNonNulls.smallBatchThreshold`,
default 64; any positive value is accepted, independently of the bitmap word
size and the `na.drop` threshold. Tests cover both strategies and
non-word-aligned thresholds. Criterion now compares row, bitmap and default
strategies on identical inputs around the boundary. Current-head timings are
still pending, so I am not claiming that 64 is optimal.
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