kosiew commented on code in PR #25534:
URL: https://github.com/apache/datafusion/pull/25534#discussion_r4062195162
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datafusion/physical-plan/src/topk/mod.rs:
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@@ -1384,9 +1368,139 @@ impl PartitionedTopK {
self.row_converter
.append(&mut self.scratch_rows, &ob_arrays)?;
- // 4. Per-partition: take the sub-batch, walk indices, dispatch
- // qualifying rows into the partition's heap.
- let k = self.k;
+ Ok(groups)
+ }
+
+ /// Take back the drained scratch map from [`Self::encode_and_group`]
+ /// (capacity retained) and record the batch's heap replacements.
+ fn finish_batch(&mut self, groups: HashMap<Vec<u8>, Vec<u32>>,
replacements: usize) {
+ self.partition_groups = groups;
+ if replacements > 0 {
+ self.metrics.row_replacements.add(replacements);
+ }
+ }
+
+ /// Emit every partition in partition-key order as a stream of coalesced
+ /// `RecordBatch`es. `emit_partition` pushes one partition's rows, already
+ /// in ORDER BY order, into the coalescer.
+ fn emit<S>(
Review Comment:
Nice refactor. One optional suggestion: could we extend
`test_partitioned_topk_output_batches_metric_counts_emitted_batches` to assert
the emitted row order as well? It already exercises multiple partitions, a
small `batch_size`, coalescing, and the output-batch metrics through the shared
`emit` path, so checking row order would round out the coverage here. Not
blocking.
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