fixed explicit types in core package

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -35,6 +35,7 @@ import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHad
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.mesos.MesosNativeLibrary
 
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
 import org.apache.spark.partial.{ApproximateEvaluator, PartialResult}
 import org.apache.spark.rdd._
@@ -209,7 +210,7 @@ class SparkContext(
   ui.start()
 
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
-  val hadoopConfiguration = {
+  val hadoopConfiguration: Configuration = {
     val env = SparkEnv.get
     val hadoopConf = SparkHadoopUtil.get.newConfiguration()
     // Explicitly check for S3 environment variables
@@ -610,7 +611,7 @@ class SparkContext(
    * standard mutable collections. So you can use this with mutable Map, Set, etc.
    */
   def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T]
-      (initialValue: R) = {
+      (initialValue: R): Accumulable[R, T] = {
     val param = new GrowableAccumulableParam[R,T]
     new Accumulable(initialValue, param)
   }
@@ -620,7 +621,7 @@ class SparkContext(
    * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
    * The variable will be sent to each cluster only once.
    */
-  def broadcast[T](value: T) = env.broadcastManager.newBroadcast[T](value, isLocal)
+  def broadcast[T](value: T): Broadcast[T] = env.broadcastManager.newBroadcast[T](value, isLocal)
 
   /**
    * Add a file to be downloaded with this Spark job on every node.
@@ -1072,7 +1073,7 @@ object SparkContext extends Logging {
   implicit def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]) = new AsyncRDDActions(rdd)
 
   implicit def rddToSequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable: ClassTag](
-      rdd: RDD[(K, V)])   =
+      rdd: RDD[(K, V)]) =
     new SequenceFileRDDFunctions(rdd)
 
   implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassTag, V: ClassTag](
@@ -1109,23 +1110,28 @@ object SparkContext extends Logging {
   }
 
   // Helper objects for converting common types to Writable
-  private def simpleWritableConverter[T, W <: Writable: ClassTag](convert: W => T) = {
+  private def simpleWritableConverter[T, W <: Writable: ClassTag](convert: W => T):
+  WritableConverter[T] = {
     val wClass = classTag[W].runtimeClass.asInstanceOf[Class[W]]
     new WritableConverter[T](_ => wClass, x => convert(x.asInstanceOf[W]))
   }
 
-  implicit def intWritableConverter() = simpleWritableConverter[Int, IntWritable](_.get)
+  implicit def intWritableConverter(): WritableConverter[Int] =
+    simpleWritableConverter[Int, IntWritable](_.get)
 
-  implicit def longWritableConverter() = simpleWritableConverter[Long, LongWritable](_.get)
+  implicit def longWritableConverter(): WritableConverter[Long] =
+    simpleWritableConverter[Long, LongWritable](_.get)
 
-  implicit def doubleWritableConverter() = simpleWritableConverter[Double, DoubleWritable](_.get)
+  implicit def doubleWritableConverter(): WritableConverter[Double] =
+    simpleWritableConverter[Double, DoubleWritable](_.get)
 
-  implicit def floatWritableConverter() = simpleWritableConverter[Float, FloatWritable](_.get)
+  implicit def floatWritableConverter(): WritableConverter[Float] =
+    simpleWritableConverter[Float, FloatWritable](_.get)
 
-  implicit def booleanWritableConverter() =
+  implicit def booleanWritableConverter(): WritableConverter[Boolean] =
     simpleWritableConverter[Boolean, BooleanWritable](_.get)
 
-  implicit def bytesWritableConverter() = {
+  implicit def bytesWritableConverter(): WritableConverter[Array[Byte]] = {
     simpleWritableConverter[Array[Byte], BytesWritable](_.getBytes)
   }
 

core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala

@@ -391,19 +391,24 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   /**
    * Save this RDD as a text file, using string representations of elements.
    */
-  def saveAsTextFile(path: String) = rdd.saveAsTextFile(path)
+  def saveAsTextFile(path: String) {
+    rdd.saveAsTextFile(path)
+  }
 
 
   /**
    * Save this RDD as a compressed text file, using string representations of elements.
    */
-  def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) =
+  def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) {
     rdd.saveAsTextFile(path, codec)
+  }
 
   /**
    * Save this RDD as a SequenceFile of serialized objects.
    */
-  def saveAsObjectFile(path: String) = rdd.saveAsObjectFile(path)
+  def saveAsObjectFile(path: String) {
+    rdd.saveAsObjectFile(path)
+  }
 
   /**
    * Creates tuples of the elements in this RDD by applying `f`.
@@ -420,7 +425,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * executed on this RDD. It is strongly recommended that this RDD is persisted in
    * memory, otherwise saving it on a file will require recomputation.
    */
-  def checkpoint() = rdd.checkpoint()
+  def checkpoint() {
+    rdd.checkpoint()
+  }
 
   /**
    * Return whether this RDD has been checkpointed or not

core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala

@@ -461,7 +461,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     sc.setCheckpointDir(dir)
   }
 
-  def getCheckpointDir = JavaUtils.optionToOptional(sc.getCheckpointDir)
+  def getCheckpointDir: Optional[String] = JavaUtils.optionToOptional(sc.getCheckpointDir)
 
   protected def checkpointFile[T](path: String): JavaRDD[T] = {
     implicit val ctag: ClassTag[T] = fakeClassTag

core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala

@@ -112,5 +112,5 @@ private[spark] class ClientArguments(args: Array[String]) {
 }
 
 object ClientArguments {
-  def isValidJarUrl(s: String) = s.matches("(.+):(.+)jar")
+  def isValidJarUrl(s: String): Boolean = s.matches("(.+):(.+)jar")
 }

core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala

@@ -32,7 +32,7 @@ import scala.collection.JavaConversions._
  * Contains util methods to interact with Hadoop from Spark.
  */
 class SparkHadoopUtil {
-  val conf = newConfiguration()
+  val conf: Configuration = newConfiguration()
   UserGroupInformation.setConfiguration(conf)
 
   def runAsUser(user: String)(func: () => Unit) {

core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala

@@ -20,16 +20,17 @@ package org.apache.spark.deploy.master
 import scala.collection.JavaConversions._
 
 import akka.serialization.Serialization
-import org.apache.zookeeper.CreateMode
 
+import org.apache.curator.framework.CuratorFramework
 import org.apache.spark.{Logging, SparkConf}
+import org.apache.zookeeper.CreateMode
 
 class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf)
   extends PersistenceEngine
   with Logging
 {
   val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status"
-  val zk = SparkCuratorUtil.newClient(conf)
+  val zk: CuratorFramework = SparkCuratorUtil.newClient(conf)
 
   SparkCuratorUtil.mkdir(zk, WORKING_DIR)
 

core/src/main/scala/org/apache/spark/metrics/sink/ConsoleSink.scala

@@ -38,7 +38,7 @@ class ConsoleSink(val property: Properties, val registry: MetricRegistry,
     case None => CONSOLE_DEFAULT_PERIOD
   }
 
-  val pollUnit = Option(property.getProperty(CONSOLE_KEY_UNIT)) match {
+  val pollUnit: TimeUnit = Option(property.getProperty(CONSOLE_KEY_UNIT)) match {
     case Some(s) => TimeUnit.valueOf(s.toUpperCase())
     case None => TimeUnit.valueOf(CONSOLE_DEFAULT_UNIT)
   }

core/src/main/scala/org/apache/spark/metrics/sink/CsvSink.scala

@@ -41,7 +41,7 @@ class CsvSink(val property: Properties, val registry: MetricRegistry,
     case None => CSV_DEFAULT_PERIOD
   }
 
-  val pollUnit = Option(property.getProperty(CSV_KEY_UNIT)) match {
+  val pollUnit: TimeUnit = Option(property.getProperty(CSV_KEY_UNIT)) match {
     case Some(s) => TimeUnit.valueOf(s.toUpperCase())
     case None => TimeUnit.valueOf(CSV_DEFAULT_UNIT)
   }

core/src/main/scala/org/apache/spark/metrics/sink/GraphiteSink.scala

@@ -39,7 +39,7 @@ class GraphiteSink(val property: Properties, val registry: MetricRegistry,
   val GRAPHITE_KEY_UNIT = "unit"
   val GRAPHITE_KEY_PREFIX = "prefix"
 
-  def propertyToOption(prop: String) = Option(property.getProperty(prop))
+  def propertyToOption(prop: String): Option[String] = Option(property.getProperty(prop))
 
   if (!propertyToOption(GRAPHITE_KEY_HOST).isDefined) {
     throw new Exception("Graphite sink requires 'host' property.")
@@ -57,7 +57,7 @@ class GraphiteSink(val property: Properties, val registry: MetricRegistry,
     case None => GRAPHITE_DEFAULT_PERIOD
   }
 
-  val pollUnit = propertyToOption(GRAPHITE_KEY_UNIT) match {
+  val pollUnit: TimeUnit = propertyToOption(GRAPHITE_KEY_UNIT) match {
     case Some(s) => TimeUnit.valueOf(s.toUpperCase())
     case None => TimeUnit.valueOf(GRAPHITE_DEFAULT_UNIT)
   }

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

@@ -103,7 +103,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
     array
   }
 
-  override val partitioner = Some(part)
+  override val partitioner: Some[Partitioner] = Some(part)
 
   override def compute(s: Partition, context: TaskContext): Iterator[(K, CoGroupCombiner)] = {
     val sparkConf = SparkEnv.get.conf

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

@@ -163,7 +163,7 @@ class HadoopRDD[K, V](
     array
   }
 
-  override def compute(theSplit: Partition, context: TaskContext) = {
+  override def compute(theSplit: Partition, context: TaskContext): InterruptibleIterator[(K, V)] = {
     val iter = new NextIterator[(K, V)] {
       val split = theSplit.asInstanceOf[HadoopPartition]
       logInfo("Input split: " + split.inputSplit)

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

@@ -116,7 +116,7 @@ class JdbcRDD[T: ClassTag](
 }
 
 object JdbcRDD {
-  def resultSetToObjectArray(rs: ResultSet) = {
+  def resultSetToObjectArray(rs: ResultSet): Array[Object] = {
     Array.tabulate[Object](rs.getMetaData.getColumnCount)(i => rs.getObject(i + 1))
   }
 }

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

@@ -80,7 +80,7 @@ class NewHadoopRDD[K, V](
     result
   }
 
-  override def compute(theSplit: Partition, context: TaskContext) = {
+  override def compute(theSplit: Partition, context: TaskContext): InterruptibleIterator[(K, V)] = {
     val iter = new Iterator[(K, V)] {
       val split = theSplit.asInstanceOf[NewHadoopPartition]
       logInfo("Input split: " + split.serializableHadoopSplit)

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

@@ -121,7 +121,7 @@ abstract class RDD[T: ClassTag](
   @transient var name: String = null
 
   /** Assign a name to this RDD */
-  def setName(_name: String) = {
+  def setName(_name: String): RDD[T] = {
     name = _name
     this
   }

core/src/main/scala/org/apache/spark/storage/StorageLevel.scala

@@ -126,15 +126,16 @@ object StorageLevel {
   val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, 2)
 
   /** Create a new StorageLevel object */
-  def apply(useDisk: Boolean, useMemory: Boolean, deserialized: Boolean, replication: Int = 1) =
+  def apply(useDisk: Boolean, useMemory: Boolean, deserialized: Boolean,
+      replication: Int = 1): StorageLevel =
     getCachedStorageLevel(new StorageLevel(useDisk, useMemory, deserialized, replication))
 
   /** Create a new StorageLevel object from its integer representation */
-  def apply(flags: Int, replication: Int) =
+  def apply(flags: Int, replication: Int): StorageLevel =
     getCachedStorageLevel(new StorageLevel(flags, replication))
 
   /** Read StorageLevel object from ObjectInput stream */
-  def apply(in: ObjectInput) = {
+  def apply(in: ObjectInput): StorageLevel = {
     val obj = new StorageLevel()
     obj.readExternal(in)
     getCachedStorageLevel(obj)

core/src/main/scala/org/apache/spark/util/Distribution.scala

@@ -19,6 +19,8 @@ package org.apache.spark.util
 
 import java.io.PrintStream
 
+import scala.collection.immutable.IndexedSeq
+
 /**
  * Util for getting some stats from a small sample of numeric values, with some handy
  * summary functions.
@@ -40,15 +42,16 @@ class Distribution(val data: Array[Double], val startIdx: Int, val endIdx: Int)
    * given from 0 to 1
    * @param probabilities
    */
-  def getQuantiles(probabilities: Traversable[Double] = defaultProbabilities) = {
+  def getQuantiles(probabilities: Traversable[Double] = defaultProbabilities):
+  IndexedSeq[Double] = {
     probabilities.toIndexedSeq.map{p:Double => data(closestIndex(p))}
   }
 
   private def closestIndex(p: Double) = {
     math.min((p * length).toInt + startIdx, endIdx - 1)
   }
 
-  def showQuantiles(out: PrintStream = System.out) = {
+  def showQuantiles(out: PrintStream = System.out) {
     out.println("min\t25%\t50%\t75%\tmax")
     getQuantiles(defaultProbabilities).foreach{q => out.print(q + "\t")}
     out.println