Github user holdenk commented on a diff in the pull request: https://github.com/apache/spark/pull/22275#discussion_r219561178 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala --- @@ -3279,34 +3280,33 @@ class Dataset[T] private[sql]( val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone withAction("collectAsArrowToPython", queryExecution) { plan => - PythonRDD.serveToStream("serve-Arrow") { out => + PythonRDD.serveToStream("serve-Arrow") { outputStream => + val out = new DataOutputStream(outputStream) val batchWriter = new ArrowBatchStreamWriter(schema, out, timeZoneId) val arrowBatchRdd = toArrowBatchRdd(plan) val numPartitions = arrowBatchRdd.partitions.length - // Store collection results for worst case of 1 to N-1 partitions - val results = new Array[Array[Array[Byte]]](numPartitions - 1) - var lastIndex = -1 // index of last partition written + // Batches ordered by (index of partition, batch # in partition) tuple + val batchOrder = new ArrayBuffer[(Int, Int)]() + var partitionCount = 0 - // Handler to eagerly write partitions to Python in order + // Handler to eagerly write batches to Python out of order def handlePartitionBatches(index: Int, arrowBatches: Array[Array[Byte]]): Unit = { - // If result is from next partition in order - if (index - 1 == lastIndex) { + if (arrowBatches.nonEmpty) { batchWriter.writeBatches(arrowBatches.iterator) - lastIndex += 1 - // Write stored partitions that come next in order - while (lastIndex < results.length && results(lastIndex) != null) { - batchWriter.writeBatches(results(lastIndex).iterator) - results(lastIndex) = null - lastIndex += 1 - } - // After last batch, end the stream - if (lastIndex == results.length) { - batchWriter.end() + arrowBatches.indices.foreach { i => batchOrder.append((index, i)) } --- End diff -- Could we call `i` something more descriptive like partition_batch_num or similar?
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