zhuqi-lucas commented on code in PR #15380:
URL: https://github.com/apache/datafusion/pull/15380#discussion_r2049125883


##########
datafusion/physical-plan/src/sorts/sort.rs:
##########
@@ -662,53 +665,152 @@ impl ExternalSorter {
         let elapsed_compute = metrics.elapsed_compute().clone();
         let _timer = elapsed_compute.timer();
 
-        // Please pay attention that any operation inside of 
`in_mem_sort_stream` will
-        // not perform any memory reservation. This is for avoiding the need 
of handling
-        // reservation failure and spilling in the middle of the sort/merge. 
The memory
-        // space for batches produced by the resulting stream will be reserved 
by the
-        // consumer of the stream.
+        // Note, in theory in memory batches should have limited size, but 
some testing
+        // cases testing the memory limit use `sort_in_place_threshold_bytes` 
to, so here we
+        // set a larger limit to avoid testing failure.
+        if self.expr.len() <= 2
+            && self.reservation.size() < 1000 * 
self.sort_in_place_threshold_bytes
+        {
+            let interleave_indices = self.build_sorted_indices(
+                self.in_mem_batches.as_slice(),
+                Arc::clone(&self.expr),
+            )?;
 
-        if self.in_mem_batches.len() == 1 {
-            let batch = self.in_mem_batches.swap_remove(0);
-            let reservation = self.reservation.take();
-            return self.sort_batch_stream(batch, metrics, reservation);
-        }
+            let batches: Vec<&RecordBatch> = 
self.in_mem_batches.iter().collect();
+            let sorted_batch = interleave_record_batch(&batches, 
&interleave_indices)?;
 
-        // If less than sort_in_place_threshold_bytes, concatenate and sort in 
place
-        if self.reservation.size() < self.sort_in_place_threshold_bytes {
-            // Concatenate memory batches together and sort
-            let batch = concat_batches(&self.schema, &self.in_mem_batches)?;
             self.in_mem_batches.clear();
             self.reservation
-                .try_resize(get_reserved_byte_for_record_batch(&batch))
+                .try_resize(get_reserved_byte_for_record_batch(&sorted_batch))
                 .map_err(Self::err_with_oom_context)?;
-            let reservation = self.reservation.take();
-            return self.sort_batch_stream(batch, metrics, reservation);
+
+            metrics.record_output(sorted_batch.num_rows());
+
+            Ok(Box::pin(RecordBatchStreamAdapter::new(
+                Arc::clone(&self.schema),
+                futures::stream::once(async { Ok(sorted_batch) }),
+            )) as SendableRecordBatchStream)
+        } else {
+            // Please pay attention that any operation inside of 
`in_mem_sort_stream` will
+            // not perform any memory reservation. This is for avoiding the 
need of handling
+            // reservation failure and spilling in the middle of the 
sort/merge. The memory
+            // space for batches produced by the resulting stream will be 
reserved by the
+            // consumer of the stream.
+
+            if self.in_mem_batches.len() == 1 {
+                let batch = self.in_mem_batches.swap_remove(0);
+                let reservation = self.reservation.take();
+                return self.sort_batch_stream(batch, metrics, reservation);
+            }
+
+            // If less than sort_in_place_threshold_bytes, concatenate and 
sort in place
+            if self.reservation.size() < self.sort_in_place_threshold_bytes {
+                // Concatenate memory batches together and sort

Review Comment:
   Thank you for the feedback @Dandandan, 
   
   Now the logic is:
   
   1. self.expr.len() <= 2, we merge all the memory batch into one batch using 
interleave , and don't send to merge streaming
   2. Other cases keep original cases.
   
   
   Because, i can't see performance gain for interleave after the sort column  
>2 from the benchmark result, so i don't change the original concatenate here.
   
   
   And more challenges now are the testing fails and memory model. Because we 
return one single batch for the above case1, so it will not have partial merge 
for many cases, only the final merge, so the memory model will fail for some 
cases.



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