Minor optimizations. Use safer take, tail methods.

core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala

@@ -40,6 +40,7 @@ private[ui] class PoolPage(parent: JobProgressTab) extends WebUIPage("pool") {
         case Some(s) => s.values.toSeq
         case None => Seq[StageInfo]()
       }
+
       val activeStagesTable =
         new StageTableBase(activeStages.sortBy(_.submissionTime).reverse, parent)
 

examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala

@@ -84,10 +84,10 @@ object TwitterAlgebirdCMS {
       if (rdd.count() != 0) {
         val partial = rdd.first()
         val partialTopK = partial.heavyHitters.map(id =>
-          (id, partial.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.slice(0, TOPK)
+          (id, partial.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.take(TOPK)
         globalCMS ++= partial
         val globalTopK = globalCMS.heavyHitters.map(id =>
-          (id, globalCMS.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.slice(0, TOPK)
+          (id, globalCMS.frequency(id).estimate)).toSeq.sortBy(_._2).reverse.take(TOPK)
         println("Approx heavy hitters at %2.2f%% threshold this batch: %s".format(PERC,
           partialTopK.mkString("[", ",", "]")))
         println("Approx heavy hitters at %2.2f%% threshold overall: %s".format(PERC,
@@ -102,7 +102,7 @@ object TwitterAlgebirdCMS {
           {case (id, count) => (count, id)})
           .sortByKey(ascending = false).take(TOPK)
         globalExact = mm.plus(globalExact.toMap, partialMap)
-        val globalTopK = globalExact.toSeq.sortBy(_._2).reverse.slice(0, TOPK)
+        val globalTopK = globalExact.toSeq.sortBy(_._2).reverse.take(TOPK)
         println("Exact heavy hitters this batch: %s".format(partialTopK.mkString("[", ",", "]")))
         println("Exact heavy hitters overall: %s".format(globalTopK.mkString("[", ",", "]")))
       }

mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala

@@ -155,7 +155,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
     val intercept = if (addIntercept) weightsWithIntercept(weightsWithIntercept.size - 1) else 0.0
     val weights =
       if (addIntercept) {
-        Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1))
+        Vectors.dense(weightsWithIntercept.toArray.tail)
       } else {
         weightsWithIntercept
       }

mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala

@@ -86,7 +86,7 @@ object MFDataGenerator {
     val mn = m * n
     val shuffled = rand.shuffle(1 to mn toList)
 
-    val omega = shuffled.slice(0, sampSize)
+    val omega = shuffled.take(sampSize)
     val ordered = omega.sortWith(_ < _).toArray
     val trainData: RDD[(Int, Int, Double)] = sc.parallelize(ordered)
       .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1)))

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala

@@ -188,7 +188,7 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
     case OrderedDistribution(requiredOrdering) =>
-      val minSize = Seq(requiredOrdering.size, ordering.size).min
+      val minSize = math.min(requiredOrdering.size, ordering.size)
       requiredOrdering.take(minSize) == ordering.take(minSize)
     case ClusteredDistribution(requiredClustering) =>
       clusteringSet.subsetOf(requiredClustering.toSet)