Dandandan commented on code in PR #12039:
URL: https://github.com/apache/datafusion/pull/12039#discussion_r1720962983
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datafusion/physical-plan/src/filter.rs:
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@@ -345,19 +360,55 @@ struct FilterExecStream {
input: SendableRecordBatchStream,
/// runtime metrics recording
baseline_metrics: BaselineMetrics,
+ /// Whether to allow an input batch to be returned unmodified in the case
where
+ /// the predicate evaluates to true for all rows in the batch
+ reuse_input_batches: bool,
}
pub(crate) fn batch_filter(
batch: &RecordBatch,
predicate: &Arc<dyn PhysicalExpr>,
+ reuse_input_batches: bool,
) -> Result<RecordBatch> {
predicate
.evaluate(batch)
.and_then(|v| v.into_array(batch.num_rows()))
.and_then(|array| {
Ok(match as_boolean_array(&array) {
// apply filter array to record batch
- Ok(filter_array) => filter_record_batch(batch, filter_array)?,
+ Ok(filter_array) => {
+ if reuse_input_batches {
+ filter_record_batch(batch, filter_array)?
+ } else {
+ if filter_array.true_count() == batch.num_rows() {
+ // special case where we just make an exact copy
Review Comment:
That would be the same that the filter.
The use case as far as I understand is that for comet the data needs to be a
new copy, as spark will reuse the existing data/arrays.
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