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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