From e45daf226d780f4a7aaabc2de9f04367bee16f26 Mon Sep 17 00:00:00 2001
From: Chris Cope <ccope@resilientscience.com>
Date: Sat, 9 Aug 2014 20:58:56 -0700
Subject: [PATCH] [SPARK-1766] sorted functions to meet pedantic requirements
Pedantry is underrated
Author: Chris Cope <ccope@resilientscience.com>
Closes #1859 from copester/master and squashes the following commits:
0fb4499 [Chris Cope] [SPARK-1766] sorted functions to meet pedantic requirements
---
.../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.
--
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