BryanCutler commented on a change in pull request #24650: [SPARK-27778][PYTHON]
Fix toPandas conversion of empty DataFrame with Arrow enabled
URL: https://github.com/apache/spark/pull/24650#discussion_r286271216
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##########
@@ -3306,27 +3303,24 @@ class Dataset[T] private[sql](
partitionBatchIndex => batchOrder.append((index,
partitionBatchIndex))
}
}
- partitionCount += 1
-
- // 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()
- }
}
+ val arrowBatchRdd = toArrowBatchRdd(plan)
sparkSession.sparkContext.runJob(
arrowBatchRdd,
- (ctx: TaskContext, it: Iterator[Array[Byte]]) => it.toArray,
- 0 until numPartitions,
- handlePartitionBatches)
+ (it: Iterator[Array[Byte]]) => it.toArray,
+ handlePartitionBatches _)
+
+ // After processing all partitions, end the stream and write batch
order indices
+ 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()
Review comment:
This `flush` could be removed now since the output stream is closed right
after this returns, which will flush it.
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