Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/1147#discussion_r15725487
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala ---
@@ -137,6 +137,185 @@ trait HashJoin {
}
/**
+ * Constant Value for Binary Join Node
+ */
+object HashOuterJoin {
+ val DUMMY_LIST = Seq[Row](null)
+ val EMPTY_LIST = Seq[Row]()
+}
+
+/**
+ * :: DeveloperApi ::
+ * Performs a hash based outer join for two child relations by shuffling
the data using
+ * the join keys. This operator requires loading the associated partition
in both side into memory.
+ */
+@DeveloperApi
+case class HashOuterJoin(
+ leftKeys: Seq[Expression],
+ rightKeys: Seq[Expression],
+ joinType: JoinType,
+ condition: Option[Expression],
+ left: SparkPlan,
+ right: SparkPlan) extends BinaryNode {
+
+ override def outputPartitioning: Partitioning = left.outputPartitioning
+
+ override def requiredChildDistribution =
+ ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) ::
Nil
+
+ def output = left.output ++ right.output
+
+ // TODO we need to rewrite all of the iterators with our own
implementation instead of the Scala
+ // iterator for performance purpose.
+
+ private[this] def leftOuterIterator(
+ key: Row, leftIter: Iterable[Row], rightIter: Iterable[Row]):
Iterator[Row] = {
+ val joinedRow = new JoinedRow()
+ val rightNullRow = new GenericRow(right.output.length)
+ val boundCondition =
+ condition.map(newPredicate(_, left.output ++
right.output)).getOrElse((row: Row) => true)
+
+ leftIter.iterator.flatMap { l =>
+ joinedRow.withLeft(l)
+ var matched = false
+ (if (!key.anyNull) rightIter.collect { case r if
(boundCondition(joinedRow.withRight(r))) =>
+ matched = true
+ joinedRow.copy
+ } else {
+ Nil
+ }) ++ HashOuterJoin.DUMMY_LIST.filter(_ => !matched).map( _ => {
+ // HashOuterJoin.DUMMY_LIST.filter(_ => !matched) is a tricky way
to add additional row,
--- End diff --
Oh wow... thanks for the explanation. That is even more subtle than I
thought.
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