Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/1499#discussion_r15556144
--- Diff:
core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala ---
@@ -0,0 +1,667 @@
+/*
+ * 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.util.collection
+
+import java.io._
+import java.util.Comparator
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable
+
+import com.google.common.io.ByteStreams
+
+import org.apache.spark.{Aggregator, SparkEnv, Logging, Partitioner}
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.storage.BlockId
+
+/**
+ * Sorts and potentially merges a number of key-value pairs of type (K, V)
to produce key-combiner
+ * pairs of type (K, C). Uses a Partitioner to first group the keys into
partitions, and then
+ * optionally sorts keys within each partition using a custom Comparator.
Can output a single
+ * partitioned file with a different byte range for each partition,
suitable for shuffle fetches.
+ *
+ * If combining is disabled, the type C must equal V -- we'll cast the
objects at the end.
+ *
+ * @param aggregator optional Aggregator with combine functions to use for
merging data
+ * @param partitioner optional Partitioner; if given, sort by partition ID
and then key
+ * @param ordering optional Ordering to sort keys within each partition;
should be a total ordering
+ * @param serializer serializer to use when spilling to disk
+ *
+ * Note that if an Ordering is given, we'll always sort using it, so only
provide it if you really
+ * want the output keys to be sorted. In a map task without map-side
combine for example, you
+ * probably want to pass None as the ordering to avoid extra sorting. On
the other hand, if you do
+ * want to do combining, having an Ordering is more efficient than not
having it.
+ *
+ * At a high level, this class works as follows:
+ *
+ * - We repeatedly fill up buffers of in-memory data, using either a
SizeTrackingAppendOnlyMap if
+ * we want to combine by key, or an simple SizeTrackingBuffer if we
don't. Inside these buffers,
+ * we sort elements of type ((Int, K), C) where the Int is the partition
ID. This is done to
+ * avoid calling the partitioner multiple times on the same key (e.g.
for RangePartitioner).
+ *
+ * - When each buffer reaches our memory limit, we spill it to a file.
This file is sorted first
+ * by partition ID and possibly second by key or by hash code of the
key, if we want to do
+ * aggregation. For each file, we track how many objects were in each
partition in memory, so we
+ * don't have to write out the partition ID for every element.
+ *
+ * - When the user requests an iterator, the spilled files are merged,
along with any remaining
+ * in-memory data, using the same sort order defined above (unless both
sorting and aggregation
+ * are disabled). If we need to aggregate by key, we either use a total
ordering from the
+ * ordering parameter, or read the keys with the same hash code and
compare them with each other
+ * for equality to merge values.
+ *
+ * - Users are expected to call stop() at the end to delete all the
intermediate files.
+ */
+private[spark] class ExternalSorter[K, V, C](
+ aggregator: Option[Aggregator[K, V, C]] = None,
+ partitioner: Option[Partitioner] = None,
+ ordering: Option[Ordering[K]] = None,
+ serializer: Option[Serializer] = None) extends Logging {
+
+ private val numPartitions = partitioner.map(_.numPartitions).getOrElse(1)
+ private val shouldPartition = numPartitions > 1
+
+ private val blockManager = SparkEnv.get.blockManager
+ private val diskBlockManager = blockManager.diskBlockManager
+ private val ser = Serializer.getSerializer(serializer)
+ private val serInstance = ser.newInstance()
+
+ private val conf = SparkEnv.get.conf
+ private val spillingEnabled = conf.getBoolean("spark.shuffle.spill",
true)
+ private val fileBufferSize = conf.getInt("spark.shuffle.file.buffer.kb",
100) * 1024
+ private val serializerBatchSize =
conf.getLong("spark.shuffle.spill.batchSize", 10000)
--- End diff --
would be great to comment on why we are doing this batching (to avoid hash
map growing too large right?)
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