Revert unnecessary changes to core

While benchmarking, we accidentally committed some unnecessary changes
to core such as adding logging. These changes make it more difficult to
merge from Spark upstream, so this commit reverts them.

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

@@ -156,7 +156,6 @@ object SparkEnv extends Logging {
 
     val serializer = serializerManager.setDefault(
       System.getProperty("spark.serializer", "org.apache.spark.serializer.JavaSerializer"))
-    logInfo("spark.serializer is " + System.getProperty("spark.serializer"))
 
     val closureSerializer = serializerManager.get(
       System.getProperty("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"))

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

@@ -245,7 +245,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)])
     if (getKeyClass().isArray && partitioner.isInstanceOf[HashPartitioner]) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
-    new ShuffledRDD[K, V, (K, V)](self, partitioner) 
+    new ShuffledRDD[K, V, (K, V)](self, partitioner)
   }
 
   /**
@@ -265,9 +265,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)])
    * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to
    * partition the output RDD.
    */
-
-  def leftOuterJoin[W: ClassManifest](other: RDD[(K, W)], partitioner: Partitioner): 
-  RDD[(K, (V, Option[W]))] = {
+  def leftOuterJoin[W: ClassManifest](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
     this.cogroup(other, partitioner).flatMapValues { case (vs, ws) =>
       if (ws.isEmpty) {
         vs.iterator.map(v => (v, None))

core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala

@@ -63,10 +63,14 @@ class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging
     kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer())
 
     // Allow the user to register their own classes by setting spark.kryo.registrator
-    Option(System.getProperty("spark.kryo.registrator")).foreach { regCls =>
-      logDebug("Running user registrator: " + regCls)
-      val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator]
-      reg.registerClasses(kryo)
+    try {
+      Option(System.getProperty("spark.kryo.registrator")).foreach { regCls =>
+        logDebug("Running user registrator: " + regCls)
+        val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator]
+        reg.registerClasses(kryo)
+      }
+    } catch {
+      case _: Exception => println("Failed to register spark.kryo.registrator")
     }
 
     // Register Chill's classes; we do this after our ranges and the user's own classes to let
@@ -118,7 +122,7 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser
   }
 }
 
-private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance with Logging {
+private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance {
   val kryo = ks.newKryo()
 
   // Make these lazy vals to avoid creating a buffer unless we use them

core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala

@@ -25,7 +25,7 @@ import java.util.concurrent.ConcurrentHashMap
  * instance of the serializer object has been created, the get method returns that instead of
  * creating a new one.
  */
-private[spark] class SerializerManager extends org.apache.spark.Logging {
+private[spark] class SerializerManager {
 
   private val serializers = new ConcurrentHashMap[String, Serializer]
   private var _default: Serializer = _