Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/4450#discussion_r29112320
--- Diff:
core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala
---
@@ -0,0 +1,117 @@
+/*
+ * 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.util.Comparator
+
+import org.apache.spark.storage.BlockObjectWriter
+
+/**
+ * A common interface for size-tracking collections of key-value pairs that
+ * - Have an associated partition for each key-value pair.
+ * - Support a memory-efficient sorted iterator
+ * - Support a WritablePartitionedIterator for writing the contents
directly as bytes.
+ */
+private[spark] trait WritablePartitionedPairCollection[K, V] extends
SizeTracker {
+ /**
+ * Insert a key-value pair with a partition into the collection
+ */
+ def insert(partition: Int, key: K, value: V): Unit
+
+ /**
+ * Estimate the collection's current memory usage in bytes.
+ */
+ def estimateSize(): Long
+
+ /**
+ * Iterate through the data in order of partition ID and then the given
comparator. This may
+ * destroy the underlying collection.
+ */
+ def partitionedDestructiveSortedIterator(keyComparator: Comparator[K]):
Iterator[((Int, K), V)]
+
+ /**
+ * Iterate through the data and write out the elements instead of
returning them. Records are
+ * returned in order of their partition ID and then the given comparator.
+ * This may destroy the underlying collection.
+ */
+ def destructiveSortedWritablePartitionedIterator(keyComparator:
Comparator[K])
+ : WritablePartitionedIterator = {
+
WritablePartitionedIterator.fromIterator(partitionedDestructiveSortedIterator(keyComparator))
+ }
+
+ /**
+ * Iterate through the data and write out the elements instead of
returning them.
+ */
+ def writablePartitionedIterator(): WritablePartitionedIterator
+}
+
+private[spark] object WritablePartitionedPairCollection {
+ /**
+ * A comparator for (Int, K) pairs that orders them by only their
partition ID.
+ */
+ def partitionComparator[K]: Comparator[(Int, K)] = new Comparator[(Int,
K)] {
+ override def compare(a: (Int, K), b: (Int, K)): Int = {
+ a._1 - b._1
+ }
+ }
+
+ /**
+ * A comparator for (Int, K) pairs that orders them both by their
partition ID and a key ordering.
+ */
+ def partitionKeyComparator[K](keyComparator: Comparator[K]):
Comparator[(Int, K)] = {
+ new Comparator[(Int, K)] {
+ override def compare(a: (Int, K), b: (Int, K)): Int = {
+ val partitionDiff = a._1 - b._1
+ if (partitionDiff != 0) {
+ partitionDiff
+ } else {
+ keyComparator.compare(a._2, b._2)
+ }
+ }
+ }
+ }
+}
+
+/**
+ * Iterator that writes elements to a BlockObjectWriter instead of
returning them. Each element
+ * has an associated partition.
+ */
+private[spark] trait WritablePartitionedIterator {
--- End diff --
This is a bit awkward the way this iterator works since it's threading the
BlockObjectWriter through everywhere. It would be much nicer if this
WritablePartitionedPairCollection could be written in a way that's less coupled.
What if you allow WritablePartitionedPairCollection to return an iterator
over serialized data and then the callers can pass that through to the block
object writer? Then you'd probably just call it `PartitionedPairCollection`.
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