Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1381#issuecomment-55509076
I came up with a more compact way to implement this, which doesn't need
casts and correctly preserves partitioning:
```diff
diff --git
a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index d0dbfef..7889269 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -76,4 +76,27 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
}
+ /**
+ * Returns an RDD containing only the elements in the the inclusive
range `lower` to `upper`.
+ * If the RDD has been partitioned using a `RangePartitioner`, then this
operation can be
+ * performed efficiently by only scanning the partitions that might
contain matching elements.
+ * Otherwise, a standard `filter` is applied to all partitions.
+ */
+ def filterByRange(lower: K, upper: K): RDD[P] = {
+
+ def inRange(k: K): Boolean = ordering.gteq(k, lower) &&
ordering.lteq(k, upper)
+
+ val rddToFilter: RDD[P] = self.partitioner match {
+ case Some(rp: RangePartitioner[K,V]) => {
+ val partitionIndicies = (rp.getPartition(lower),
rp.getPartition(upper)) match {
+ case (l, u) if l <= u => l to u
+ case (l, u) if l > u => u to l
+ }
+ PartitionPruningRDD.create(self, partitionIndicies.contains)
+ }
+ case _ =>
+ self
+ }
+ rddToFilter.filter { case (k, v) => inRange(k) }
+ }
}
```
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