Github user lianhuiwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/3505#discussion_r22774231
--- Diff: core/src/main/scala/org/apache/spark/rdd/SkewedJoinRDD.scala ---
@@ -0,0 +1,345 @@
+/*
+ * 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.rdd
+
+import java.io.{ObjectOutputStream, IOException}
+
+import scala.reflect.ClassTag
+
+import org.apache.spark._
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle.ShuffleHandle
+import org.apache.spark.util.Utils
+import org.apache.spark.util.collection._
+
+private[spark] sealed trait JoinType[K, L, R, PAIR <: Product2[_, _]]
extends Serializable {
+
+ def flatten(i: Iterator[(K, (Iterable[Chunk[L]], Iterable[Chunk[R]]))]):
Iterator[(K, PAIR)]
+
+}
+
+private[spark] object JoinType {
+
+ def inner[K, L, R] = new JoinType[K, L, R, (L, R)] {
+
+ override def flatten(i: Iterator[(K, (Iterable[Chunk[L]],
Iterable[Chunk[R]]))]) =
+ i flatMap {
+ case (key, pair) => {
+ if (pair._1.size < pair._2.size) {
+ yieldPair(pair._1, pair._2, key, (p1: L, p2: R) => (p1, p2))
+ } else {
+ yieldPair(pair._2, pair._1, key, (p2: R, p1: L) => (p1, p2))
+ }
+ }
+ }
+ }
+
+ def leftOuter[K, L, R] = new JoinType[K, L, R, (L, Option[R])] {
+
+ override def flatten(i: Iterator[(K, (Iterable[Chunk[L]],
Iterable[Chunk[R]]))]) =
+ i flatMap {
+ case (key, pair) => {
+ if (pair._2.size == 0) {
+ for (chunk <- pair._1.iterator;
+ v <- chunk
+ ) yield (key, (v, None)): (K, (L, Option[R]))
+ }
+ else if (pair._1.size < pair._2.size) {
+ yieldPair(pair._1, pair._2, key, (p1: L, p2: R) => (p1,
Some(p2)))
+ } else {
+ yieldPair(pair._2, pair._1, key, (p2: R, p1: L) => (p1,
Some(p2)))
+ }
+ }
+ }
+ }
+
+ def rightOuter[K, L, R] = new JoinType[K, L, R, (Option[L], R)] {
+
+ override def flatten(i: Iterator[(K, (Iterable[Chunk[L]],
Iterable[Chunk[R]]))]) =
+ i flatMap {
+ case (key, pair) => {
+ if (pair._1.size == 0) {
+ for (chunk <- pair._2.iterator;
+ v <- chunk
+ ) yield (key, (None, v)): (K, (Option[L], R))
+ }
+ else if (pair._1.size < pair._2.size) {
+ yieldPair(pair._1, pair._2, key, (p1: L, p2: R) => (Some(p1),
p2))
+ } else {
+ yieldPair(pair._2, pair._1, key, (p2: R, p1: L) => (Some(p1),
p2))
+ }
+ }
+ }
+ }
+
+ def fullOuter[K, L, R] = new JoinType[K, L, R, (Option[L], Option[R])] {
+
+ override def flatten(i: Iterator[(K, (Iterable[Chunk[L]],
Iterable[Chunk[R]]))]) =
+ i flatMap {
+ case (key, pair) => {
+ if (pair._1.size == 0) {
+ for (chunk <- pair._2.iterator;
+ v <- chunk
+ ) yield (key, (None, Some(v))): (K, (Option[L], Option[R]))
+ }
+ else if (pair._2.size == 0) {
+ for (chunk <- pair._1.iterator;
+ v <- chunk
+ ) yield (key, (Some(v), None)): (K, (Option[L], Option[R]))
+ }
+ else if (pair._1.size < pair._2.size) {
+ yieldPair(pair._1, pair._2, key, (p1: L, p2: R) => (Some(p1),
Some(p2)))
+ } else {
+ yieldPair(pair._2, pair._1, key, (p2: R, p1: L) => (Some(p1),
Some(p2)))
+ }
+ }
+ }
+ }
+
+ private def yieldPair[K, OUT, IN, PAIR <: Product2[_, _]](
+ outer: Iterable[Chunk[OUT]], inner: Iterable[Chunk[IN]], key: K,
toPair: (OUT, IN) => PAIR) =
+ for (
+ outerChunk <- outer.iterator;
+ innerChunk <- inner.iterator;
+ outerValue <- outerChunk;
+ innerValue <- innerChunk
+ ) yield (key, toPair(outerValue, innerValue))
+}
+
+private[spark] sealed trait JoinSplitDep extends Serializable
+
+private[spark] case class NarrowJoinSplitDep(
+ rdd: RDD[_],
+ splitIndex: Int,
+ var split: Partition) extends JoinSplitDep {
+
+ @throws(classOf[IOException])
+ private def writeObject(oos: ObjectOutputStream): Unit =
Utils.tryOrIOException {
+ // Update the reference to parent split at the time of task
serialization
+ split = rdd.partitions(splitIndex)
+ oos.defaultWriteObject()
+ }
+}
+
+private[spark] case class ShuffleJoinSplitDep(handle: ShuffleHandle)
extends JoinSplitDep
+
+private[spark] class JoinPartition(idx: Int, val left: JoinSplitDep, val
right: JoinSplitDep)
+ extends Partition with Serializable {
+ override val index: Int = idx
+
+ override def hashCode(): Int = idx
+}
+
+private[spark] class BufferWrapper private(val isChunkBuffer: Boolean,
buffer: Iterable[Any])
+ extends Serializable {
+
+ def this(buffer: CompactBuffer[_]) {
+ this(false, buffer)
+ }
+
+ def this(buffer: ChunkBuffer[_]) {
+ this(true, buffer)
+ }
+
+ def getChunkBuffer[T: ClassTag]: ChunkBuffer[T] =
buffer.asInstanceOf[ChunkBuffer[T]]
+
+ def getCompactBuffer[T: ClassTag]: CompactBuffer[T] =
buffer.asInstanceOf[CompactBuffer[T]]
+
+ def asChunkIterable[T: ClassTag]: Iterable[Chunk[T]] = {
+ if (isChunkBuffer) {
+ getChunkBuffer[T]
+ }
+ else {
+ val buffer = getCompactBuffer[T]
+ if (buffer.isEmpty) {
+ Iterable[Chunk[T]]()
+ }
+ else {
+ Iterable(new Chunk[T](buffer))
+ }
+ }
+ }
+}
+
+private[spark] class SkewedJoinRDD[K, L, R, PAIR <: Product2[_, _]](
+ left: RDD[(K, L)], right: RDD[(K, R)], part: Partitioner, joinType:
JoinType[K, L, R, PAIR])
+ (implicit kt: ClassTag[K], lt: ClassTag[L], rt: ClassTag[R], ord:
Ordering[K])
--- End diff --
no, i just replace ExternalAppendOnlyMap with ExternalOrderingAppendOnlyMap
in Aggregator. but i find ExternalOrderingAppendOnlyMap is not common for
skewedGroupby and skewedJoin. in skewedJoin key in
ExternalOrderingAppendOnlyMap is a ordering. how about we can make
ExternalOrderingAppendOnlyMap be general, all of skewedJoin and skewedGroupby
can resolve skew problem using it? @zsxwing
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