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author | Chris Cope <ccope@resilientscience.com> | 2014-08-09 20:58:56 -0700 |
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committer | Patrick Wendell <pwendell@gmail.com> | 2014-08-09 20:58:56 -0700 |
commit | e45daf226d780f4a7aaabc2de9f04367bee16f26 (patch) | |
tree | 79122ee8c6bb1fe1b41adf40ac5af4dcd77ec82c /core | |
parent | b431e6747f410aaf9624585920adc1f303159861 (diff) | |
download | spark-e45daf226d780f4a7aaabc2de9f04367bee16f26.tar.gz spark-e45daf226d780f4a7aaabc2de9f04367bee16f26.tar.bz2 spark-e45daf226d780f4a7aaabc2de9f04367bee16f26.zip |
[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
Diffstat (limited to 'core')
-rw-r--r-- | core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala | 38 |
1 files 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 @@ -238,6 +238,25 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) } /** + * 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 * before sending results to a reducer, similarly to a "combiner" in MapReduce. @@ -375,15 +394,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) } /** - * 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. * @@ -483,16 +493,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) } /** - * 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. * |