[GitHub] [spark] HyukjinKwon commented on a change in pull request #24650: [SPARK-27778][PySpark] Fix toPandas conversion using arrow for DFs with no partitions

[GitBox <gi...@apache.org>]

HyukjinKwon commented on a change in pull request #24650: [SPARK-27778][PySpark] Fix toPandas conversion using arrow for DFs with no partitions
URL: https://github.com/apache/spark/pull/24650#discussion_r285839287
 
 

 ##########
 File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
 ##########
 @@ -3310,23 +3310,31 @@ class Dataset[T] private[sql](
 
           // After last batch, end the stream and write batch order indices
           if (partitionCount == numPartitions) {
-            batchWriter.end()
-            out.writeInt(batchOrder.length)
-            // Sort by (index of partition, batch index in that partition) tuple to get the
-            // overall_batch_index from 0 to N-1 batches, which can be used to put the
-            // transferred batches in the correct order
-            batchOrder.zipWithIndex.sortBy(_._1).foreach { case (_, overallBatchIndex) =>
-              out.writeInt(overallBatchIndex)
-            }
-            out.flush()
+            doAfterLastPartition()
+          }
+        }
+
+        def doAfterLastPartition(): Unit = {
+          batchWriter.end()
+          out.writeInt(batchOrder.length)
+          // Sort by (index of partition, batch index in that partition) tuple to get the
+          // overall_batch_index from 0 to N-1 batches, which can be used to put the
+          // transferred batches in the correct order
+          batchOrder.zipWithIndex.sortBy(_._1).foreach { case (_, overallBatchIndex) =>
+            out.writeInt(overallBatchIndex)
           }
+          out.flush()
         }
 
         sparkSession.sparkContext.runJob(
           arrowBatchRdd,
           (ctx: TaskContext, it: Iterator[Array[Byte]]) => it.toArray,
           0 until numPartitions,
           handlePartitionBatches)
+
+        if (numPartitions == 0) {
 
 Review comment:
   This method is well-commented. Can you add another comment that we should end stream when partitions are empty?
   
   Also, I would do:
   
   ```scala
   partitions = 0 until numPartitions
   sparkSession.sparkContext.runJob(
     arrowBatchRdd,
     (ctx: TaskContext, it: Iterator[Array[Byte]]) => it.toArray,
     partitions,
     handlePartitionBatches)
   
   if (partitions.isEmpty) {
     // Currently result handler is not called when given partitions are empty.
     // Therefore, we should end stream here.
     doAfterLastPartition()
   }
   ```

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