Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6397#discussion_r31006530
--- Diff:
core/src/main/scala/org/apache/spark/shuffle/sort/BypassMergeSortShuffleWriter.scala
---
@@ -0,0 +1,132 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.sort
+
+import java.io.{File, FileInputStream, FileOutputStream}
+
+import org.apache.spark._
+import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.storage.{BlockId, BlockManager, BlockObjectWriter}
+import org.apache.spark.util.Utils
+import org.apache.spark.util.collection._
+
+/**
+ * This class handles sort-based shuffle's `bypassMergeSort` write path,
which is used for shuffles
+ * for which no Ordering and no Aggregator is given and the number of
partitions is
+ * less than `spark.shuffle.sort.bypassMergeThreshold`.
+ *
+ * This path used to be part of [[ExternalSorter]] but was refactored into
its own class in order to
+ * reduce code complexity; see SPARK-7855 for more details.
+ *
+ * There have been proposals to completely remove this code path; see
SPARK-6026 for details.
+ */
+private[spark] class BypassMergeSortShuffleWriter[K, V](
+ conf: SparkConf,
+ blockManager: BlockManager,
+ partitioner: Partitioner,
+ writeMetrics: ShuffleWriteMetrics,
+ serializer: Serializer)
+ extends Logging with SortShuffleFileWriter[K, V] {
+
+ private[this] val numPartitions = partitioner.numPartitions
+
+ /** Array of file writers for each partition */
+ private[this] var partitionWriters: Array[BlockObjectWriter] = _
+
+ def insertAll(records: Iterator[_ <: Product2[K, V]]): Unit = {
+ assert (partitionWriters == null)
+ if (records.hasNext) {
+ val serInstance = serializer.newInstance()
+ // Use getSizeAsKb (not bytes) to maintain backwards compatibility
if no units are provided
+ val fileBufferSize = conf.getSizeAsKb("spark.shuffle.file.buffer",
"32k").toInt * 1024
+ val openStartTime = System.nanoTime
+ partitionWriters = Array.fill(numPartitions) {
+ val (blockId, file) =
blockManager.diskBlockManager.createTempShuffleBlock()
+ val writer =
+ blockManager.getDiskWriter(blockId, file, serInstance,
fileBufferSize, writeMetrics)
+ writer.open()
+ }
+ // Creating the file to write to and creating a disk writer both
involve interacting with
+ // the disk, and can take a long time in aggregate when we open many
files, so should be
+ // included in the shuffle write time.
+ writeMetrics.incShuffleWriteTime(System.nanoTime - openStartTime)
+
+ while (records.hasNext) {
+ val record = records.next()
+ val key: K = record._1
+ partitionWriters(partitioner.getPartition(key)).write(key,
record._2)
+ }
+ }
+ }
+
+ /**
+ * Write all the data added into this writer into a single file in the
disk store. This is
+ * called by the SortShuffleWriter and can go through an efficient path
of just concatenating
+ * the per-partition binary files.
+ *
+ * @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, file:
File): Array[Long] = {
+ if (partitionWriters == null) {
+ // We were passed an empty iterator
+ Array.fill(numPartitions)(0L)
+ } else {
+ partitionWriters.foreach(_.commitAndClose())
+
+ // Track location of each range in the output file
+ val lengths = new Array[Long](numPartitions)
+
+ val transferToEnabled = conf.getBoolean("spark.file.transferTo",
true)
+ val out = new FileOutputStream(file, true)
+ val writeStartTime = System.nanoTime
+ Utils.tryWithSafeFinally {
+ for (i <- 0 until numPartitions) {
--- End diff --
I think that this was a carryover from the old code, but I don't mind
changing it here.
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