Github user sarutak commented on a diff in the pull request:
https://github.com/apache/spark/pull/2939#discussion_r19392161
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/RangeJoinImpl.scala ---
@@ -0,0 +1,64 @@
+/*
+ * 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.sql.execution
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{sql, SparkContext}
+import org.apache.spark.SparkContext._
+import scala.reflect.ClassTag
+
+object RangeJoinImpl extends Serializable{
+
+ /**
+ * Multi-joins together two RDDs that contain objects that map to
reference regions.
+ * The elements from the first RDD become the key of the output RDD, and
the value
+ * contains all elements from the second RDD which overlap the region of
the key.
+ * This is a multi-join, so it preserves n-to-m relationships between
regions.
+ *
+ * @param sc A spark context from the cluster that will perform the join
+ * @param rdd1 RDD of values on which we build an interval tree. Assume
|rdd1| < |rdd2|
+ */
+ def overlapJoin(sc: SparkContext,
+ rdd1: RDD[(Interval[Long],sql.Row)],
+ rdd2: RDD[(Interval[Long],sql.Row)]):
RDD[(sql.Row, Iterable[sql.Row])] =
+ {
+
+ val indexedRdd1 = rdd1.zipWithIndex().map(_.swap)
+
+ /*Collect only Reference regions and the index of indexedRdd1*/
+ val localIntervals = indexedRdd1.map(x => (x._2._1, x._1)).collect()
+ /*Create and broadcast an interval tree*/
+ val intervalTree = sc.broadcast(new
IntervalTree[Long](localIntervals.toList))
+
+ val kvrdd2: RDD[(Long, Iterable[sql.Row])] = rdd2
+ //join entry with the intervals returned from the interval tree
+ .map(x => (intervalTree.value.getAllOverlappings(x._1), x._2))
+ .filter(x => x._1 != Nil) //filter out entries that do not join
anywhere
+ .flatMap(t => t._1.map(s => (s._2, t._2))) //create pairs of
(index1, rdd2Elem)
+ .groupByKey
+
+
--- End diff --
extra new line.
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