From 1d413ce877a67379a0a74afefba071c018b0ca70 Mon Sep 17 00:00:00 2001
From: Doris Xin <doris.s.xin@gmail.com>
Date: Mon, 9 Jun 2014 14:14:26 -0700
Subject: [PATCH] fixed checkstyle issues

---
 .../scala/org/apache/spark/rdd/PairRDDFunctions.scala |  8 ++++----
 core/src/main/scala/org/apache/spark/rdd/RDD.scala    | 11 +++++++----
 2 files changed, 11 insertions(+), 8 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 53219d1a94b79..5fa4ce751b545 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -266,10 +266,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     }
 
     val combOp = (r1: Result, r2: Result) => {
-      //take union of both key sets in case one partion doesn't contain all keys
+      // take union of both key sets in case one partion doesn't contain all keys
       val keyUnion = r1.resultMap.keys.toSet.union(r2.resultMap.keys.toSet)
 
-      //Use r2 to keep the combined result since r1 is usual empty
+      // Use r2 to keep the combined result since r1 is usual empty
       for (key <- keyUnion) {
         val entry1 = r1.resultMap.get(key)
         val entry2 = r2.resultMap.get(key)
@@ -286,7 +286,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
     val zeroU = new Result(Map[K, Stratum]())
 
-    //determine threshold for each stratum and resample
+    // determine threshold for each stratum and resample
     val finalResult = self.aggregateWithContext(zeroU)(seqOp, combOp).resultMap
     val thresholdByKey = new mutable.HashMap[K, Double]()
     for ((key, stratum) <- finalResult) {
@@ -330,7 +330,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       // Bernoulli sampler
       self.mapPartitionsWithIndex((idx: Int, iter: Iterator[(K, V)]) => {
         val random = new RandomDataGenerator
-        random.reSeed(seed+idx)
+        random.reSeed(seed + idx)
         iter.filter(t => random.nextUniform(0.0, 1.0) < thresholdByKey.get(t._1).get)
       }, preservesPartitioning = true)
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 76418283486aa..41889c96dd28c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -884,15 +884,18 @@ abstract class RDD[T: ClassTag](
    * A version of {@link #aggregate()} that passes the TaskContext to the function that does
    * aggregation for each partition.
    */
-  def aggregateWithContext[U: ClassTag](zeroValue: U)(seqOp: ((TaskContext, U), T) => U, combOp: (U, U) => U): U = {
+  def aggregateWithContext[U: ClassTag](zeroValue: U)(seqOp: ((TaskContext, U), T) => U,
+      combOp: (U, U) => U): U = {
     // Clone the zero value since we will also be serializing it as part of tasks
     var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance())
-    //pad seqOp and combOp with taskContext to conform to aggregate's signature in TraversableOnce
+    // pad seqOp and combOp with taskContext to conform to aggregate's signature in TraversableOnce
     val paddedSeqOp = (arg1: (TaskContext, U), item: T) => (arg1._1, seqOp(arg1, item))
-    val paddedcombOp = (arg1 : (TaskContext, U), arg2: (TaskContext, U)) => (arg1._1, combOp(arg1._2, arg1._2))
+    val paddedcombOp = (arg1 : (TaskContext, U), arg2: (TaskContext, U)) =>
+      (arg1._1, combOp(arg1._2, arg1._2))
     val cleanSeqOp = sc.clean(paddedSeqOp)
     val cleanCombOp = sc.clean(paddedcombOp)
-    val aggregatePartition = (tc: TaskContext, it: Iterator[T]) => (it.aggregate(tc, zeroValue)(cleanSeqOp, cleanCombOp))._2
+    val aggregatePartition = (tc: TaskContext, it: Iterator[T]) =>
+      (it.aggregate(tc, zeroValue)(cleanSeqOp, cleanCombOp))._2
     val mergeResult = (index: Int, taskResult: U) => jobResult = combOp(jobResult, taskResult)
     sc.runJob(this, aggregatePartition, mergeResult)
     jobResult