From e45daf226d780f4a7aaabc2de9f04367bee16f26 Mon Sep 17 00:00:00 2001 From: Chris Cope Date: Sat, 9 Aug 2014 20:58:56 -0700 Subject: [SPARK-1766] sorted functions to meet pedantic requirements Pedantry is underrated Author: Chris Cope Closes #1859 from copester/master and squashes the following commits: 0fb4499 [Chris Cope] [SPARK-1766] sorted functions to meet pedantic requirements --- .../org/apache/spark/rdd/PairRDDFunctions.scala | 38 +++++++++++----------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 93af50c0a9..5dd6472b07 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -237,6 +237,25 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) combineByKey[V]((v: V) => v, func, func, partitioner) } + /** + * Merge the values for each key using an associative reduce function. This will also perform + * the merging locally on each mapper before sending results to a reducer, similarly to a + * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. + */ + def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = { + reduceByKey(new HashPartitioner(numPartitions), func) + } + + /** + * Merge the values for each key using an associative reduce function. This will also perform + * the merging locally on each mapper before sending results to a reducer, similarly to a + * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ + * parallelism level. + */ + def reduceByKey(func: (V, V) => V): RDD[(K, V)] = { + reduceByKey(defaultPartitioner(self), func) + } + /** * Merge the values for each key using an associative reduce function, but return the results * immediately to the master as a Map. This will also perform the merging locally on each mapper @@ -374,15 +393,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) countApproxDistinctByKey(relativeSD, defaultPartitioner(self)) } - /** - * Merge the values for each key using an associative reduce function. This will also perform - * the merging locally on each mapper before sending results to a reducer, similarly to a - * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. - */ - def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = { - reduceByKey(new HashPartitioner(numPartitions), func) - } - /** * Group the values for each key in the RDD into a single sequence. Allows controlling the * partitioning of the resulting key-value pair RDD by passing a Partitioner. @@ -482,16 +492,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self)) } - /** - * Merge the values for each key using an associative reduce function. This will also perform - * the merging locally on each mapper before sending results to a reducer, similarly to a - * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ - * parallelism level. - */ - def reduceByKey(func: (V, V) => V): RDD[(K, V)] = { - reduceByKey(defaultPartitioner(self), func) - } - /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with the existing partitioner/parallelism level. -- cgit v1.2.3