Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/6883#discussion_r32776196
--- Diff: core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
---
@@ -515,6 +515,76 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
}
/**
+ * Same as join, but uses a block join, otherwise known as a replicate
fragment join.
+ * This is useful in cases where the data has extreme skew.
+ * The input params leftReplication and rightReplication control the
replication of the left
+ * (this rdd) and right (other rdd) respectively.
+ */
+ def blockJoin[W](other: JavaPairRDD[K, W], leftReplication: Int,
rightReplication: Int,
+ partitioner: Partitioner): JavaPairRDD[K, (V, W)] = {
+ fromRDD(rdd.blockJoin(other, leftReplication, rightReplication,
partitioner))
+ }
+
+ /**
+ * Same as join, but uses a block join, otherwise known as a replicate
fragment join.
+ * This is useful in cases where the data has extreme skew.
+ * The input params leftReplication and rightReplication control the
replication of the left
+ * (this rdd) and right (other rdd) respectively.
+ */
+ def blockJoin[W](other: JavaPairRDD[K, W], leftReplication: Int,
rightReplication: Int)
+ : JavaPairRDD[K, (V, W)] = {
+ fromRDD(rdd.blockJoin(other, leftReplication, rightReplication))
+ }
+
+ /**
+ * Same as leftOuterJoin, but uses a block join, otherwise known as a
replicate fragment join.
+ * This is useful in cases where the data has extreme skew.
+ * The input param rightReplication controls the replication of the
right (other rdd).
+ */
+ def blockLeftOuterJoin[W](other: JavaPairRDD[K, W], rightReplication:
Int,
+ partitioner: Partitioner): JavaPairRDD[K, (V, Optional[W])] = {
+ fromRDD(rdd.blockLeftOuterJoin(other, rightReplication,
partitioner).mapValues{ case (v, w) =>
--- End diff --
need space after `.mapValues`
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