Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/1799#discussion_r15898826
--- Diff:
core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala ---
@@ -640,9 +713,122 @@ private[spark] class ExternalSorter[K, V, C](
*/
def iterator: Iterator[Product2[K, C]] =
partitionedIterator.flatMap(pair => pair._2)
+ /**
+ * Write all the data added into this ExternalSorter into a file in the
disk store, creating
+ * an .index file for it as well with the offsets of each partition.
This is called by the
+ * SortShuffleWriter and can go through an efficient path of just
concatenating binary files
+ * if we decided to avoid merge-sorting.
+ *
+ * @param blockId block ID to write to. The index file will be
blockId.name + ".index".
+ * @param context a TaskContext for a running Spark task, for us to
update shuffle metrics.
+ * @return array of lengths, in bytes, of each partition of the file
(used by map output tracker)
+ */
+ def writePartitionedFile(blockId: BlockId, context: TaskContext):
Array[Long] = {
+ val outputFile = blockManager.diskBlockManager.getFile(blockId)
+
+ // Track location of each range in the output file
+ val offsets = new Array[Long](numPartitions + 1)
+ val lengths = new Array[Long](numPartitions)
+
+ // Statistics
+ var totalBytes = 0L
+ var totalTime = 0L
+
+ if (bypassMergeSort && partitionWriters != null) {
+ // We decided to write separate files for each partition, so just
concatenate them. To keep
+ // this simple we spill out the current in-memory collection so that
everything is in files.
+ spillToPartitionFiles(if (aggregator.isDefined) map else buffer)
+ partitionWriters.foreach(_.commitAndClose())
+ var out: FileOutputStream = null
+ var in: FileInputStream = null
+ try {
+ out = new FileOutputStream(outputFile)
+ for (i <- 0 until numPartitions) {
+ val file = partitionWriters(i).fileSegment().file
--- End diff --
I find this part that uses fileSegments slightly convoluted. But we can
deal with this later.
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