Replace use of .size with .length for Arrays.

Invoking .size on arrays is valid, but requires an implicit conversion to SeqLike. This incurs a compile time overhead and more importantly a runtime overhead, as the Array must be wrapped before the method can be invoked. For example, the difference in generated byte code is:

  public int withSize();
    Code:
       0: getstatic     #23                 // Field scala/Predef$.MODULE$:Lscala/Predef$;
       3: aload_0
       4: invokevirtual #25                 // Method array:()[I
       7: invokevirtual #29                 // Method scala/Predef$.intArrayOps:([I)Lscala/collection/mutable/ArrayOps;
      10: invokeinterface #34,  1           // InterfaceMethod scala/collection/mutable/ArrayOps.size:()I
      15: ireturn

  public int withLength();
    Code:
       0: aload_0
       1: invokevirtual #25                 // Method array:()[I
       4: arraylength
       5: ireturn

Author: sksamuel

core/src/main/scala/org/apache/spark/network/nio/Connection.scala

@@ -181,7 +181,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
     buffer.get(bytes)
     bytes.foreach(x => print(x + " "))
     buffer.position(curPosition)
-    print(" (" + bytes.size + ")")
+    print(" (" + bytes.length + ")")
   }
 
   def printBuffer(buffer: ByteBuffer, position: Int, length: Int) {

core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala

@@ -45,7 +45,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
         }
         result
       },
-      Range(0, self.partitions.size),
+      Range(0, self.partitions.length),
       (index: Int, data: Long) => totalCount.addAndGet(data),
       totalCount.get())
   }
@@ -54,8 +54,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
    * Returns a future for retrieving all elements of this RDD.
    */
   def collectAsync(): FutureAction[Seq[T]] = {
-    val results = new Array[Array[T]](self.partitions.size)
-    self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.size),
+    val results = new Array[Array[T]](self.partitions.length)
+    self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length),
       (index, data) => results(index) = data, results.flatten.toSeq)
   }
 
@@ -111,15 +111,15 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
    */
   def foreachAsync(f: T => Unit): FutureAction[Unit] = {
     val cleanF = self.context.clean(f)
-    self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.size),
+    self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length),
       (index, data) => Unit, Unit)
   }
 
   /**
    * Applies a function f to each partition of this RDD.
    */
   def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = {
-    self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.size),
+    self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length),
       (index, data) => Unit, Unit)
   }
 }

core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala

@@ -36,7 +36,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
 
   override def getPartitions: Array[Partition] = {
     assertValid()
-    (0 until blockIds.size).map(i => {
+    (0 until blockIds.length).map(i => {
       new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition]
     }).toArray
   }

core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala

@@ -53,11 +53,11 @@ class CartesianRDD[T: ClassTag, U: ClassTag](
   extends RDD[Pair[T, U]](sc, Nil)
   with Serializable {
 
-  val numPartitionsInRdd2 = rdd2.partitions.size
+  val numPartitionsInRdd2 = rdd2.partitions.length
 
   override def getPartitions: Array[Partition] = {
     // create the cross product split
-    val array = new Array[Partition](rdd1.partitions.size * rdd2.partitions.size)
+    val array = new Array[Partition](rdd1.partitions.length * rdd2.partitions.length)
     for (s1 <- rdd1.partitions; s2 <- rdd2.partitions) {
       val idx = s1.index * numPartitionsInRdd2 + s2.index
       array(idx) = new CartesianPartition(idx, rdd1, rdd2, s1.index, s2.index)

core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala

@@ -49,7 +49,7 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
     if (fs.exists(cpath)) {
       val dirContents = fs.listStatus(cpath).map(_.getPath)
       val partitionFiles = dirContents.filter(_.getName.startsWith("part-")).map(_.toString).sorted
-      val numPart =  partitionFiles.size
+      val numPart =  partitionFiles.length
       if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) ||
           ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) {
         throw new SparkException("Invalid checkpoint directory: " + checkpointPath)

core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala

@@ -99,7 +99,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
 
   override def getPartitions: Array[Partition] = {
     val array = new Array[Partition](part.numPartitions)
-    for (i <- 0 until array.size) {
+    for (i <- 0 until array.length) {
       // Each CoGroupPartition will have a dependency per contributing RDD
       array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) =>
         // Assume each RDD contributed a single dependency, and get it
@@ -120,7 +120,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
     val sparkConf = SparkEnv.get.conf
     val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true)
     val split = s.asInstanceOf[CoGroupPartition]
-    val numRdds = split.deps.size
+    val numRdds = split.deps.length
 
     // A list of (rdd iterator, dependency number) pairs
     val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)]

core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala

@@ -166,7 +166,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack:
 
   // determines the tradeoff between load-balancing the partitions sizes and their locality
   // e.g. balanceSlack=0.10 means that it allows up to 10% imbalance in favor of locality
-  val slack = (balanceSlack * prev.partitions.size).toInt
+  val slack = (balanceSlack * prev.partitions.length).toInt
 
   var noLocality = true  // if true if no preferredLocations exists for parent RDD
 

core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala

@@ -70,7 +70,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
   @Experimental
   def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
-    val evaluator = new MeanEvaluator(self.partitions.size, confidence)
+    val evaluator = new MeanEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
   }
 
@@ -81,7 +81,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
   @Experimental
   def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
-    val evaluator = new SumEvaluator(self.partitions.size, confidence)
+    val evaluator = new SumEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
   }
 

core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala

@@ -56,7 +56,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * order of the keys).
    */
   // TODO: this currently doesn't work on P other than Tuple2!
-  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size)
+  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)
       : RDD[(K, V)] =
   {
     val part = new RangePartitioner(numPartitions, self, ascending)

core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala

@@ -823,7 +823,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * RDD will be <= us.
    */
   def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] =
-    subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.size)))
+    subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length)))
 
   /** Return an RDD with the pairs from `this` whose keys are not in `other`. */
   def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] =