diff options
| -rw-r--r-- | core/src/main/scala/spark/api/java/JavaPairRDD.scala | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/core/src/main/scala/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/spark/api/java/JavaPairRDD.scala index df3af3817d..c41207773e 100644 --- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala @@ -59,7 +59,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif /** * Return a new RDD containing only the elements that satisfy a predicate. */ - def filter(f: Function[(K, V), java.lang.Boolean]): JavaPairRDD[K, V] = + def filter(f: JFunction[(K, V), java.lang.Boolean]): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.filter(x => f(x).booleanValue())) /** @@ -102,7 +102,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * In addition, users can control the partitioning of the output RDD, and whether to perform * map-side aggregation (if a mapper can produce multiple items with the same key). */ - def combineByKey[C](createCombiner: Function[V, C], + def combineByKey[C](createCombiner: JFunction[V, C], mergeValue: JFunction2[C, V, C], mergeCombiners: JFunction2[C, C, C], partitioner: Partitioner): JavaPairRDD[K, C] = { @@ -309,7 +309,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * Pass each value in the key-value pair RDD through a map function without changing the keys; * this also retains the original RDD's partitioning. */ - def mapValues[U](f: Function[V, U]): JavaPairRDD[K, U] = { + def mapValues[U](f: JFunction[V, U]): JavaPairRDD[K, U] = { implicit val cm: ClassManifest[U] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] fromRDD(rdd.mapValues(f)) |