DeveloperAPI -> DeveloperApi

Author: andrewor14

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

@@ -17,18 +17,18 @@
 
 package org.apache.spark
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 import org.apache.spark.util.collection.{AppendOnlyMap, ExternalAppendOnlyMap}
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * A set of functions used to aggregate data.
  *
  * @param createCombiner function to create the initial value of the aggregation.
  * @param mergeValue function to merge a new value into the aggregation result.
  * @param mergeCombiners function to merge outputs from multiple mergeValue function.
  */
-@DeveloperAPI
+@DeveloperApi
 case class Aggregator[K, V, C] (
     createCombiner: V => C,
     mergeValue: (C, V) => C,

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

@@ -17,24 +17,24 @@
 
 package org.apache.spark
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Base class for dependencies.
  */
-@DeveloperAPI
+@DeveloperApi
 abstract class Dependency[T](val rdd: RDD[T]) extends Serializable
 
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Base class for dependencies where each partition of the parent RDD is used by at most one
  * partition of the child RDD.  Narrow dependencies allow for pipelined execution.
  */
-@DeveloperAPI
+@DeveloperApi
 abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
   /**
    * Get the parent partitions for a child partition.
@@ -46,15 +46,15 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
 
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Represents a dependency on the output of a shuffle stage.
  * @param rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
  * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to null,
  *                   the default serializer, as specified by `spark.serializer` config option, will
  *                   be used.
  */
-@DeveloperAPI
+@DeveloperApi
 class ShuffleDependency[K, V](
     @transient rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
@@ -66,24 +66,24 @@ class ShuffleDependency[K, V](
 
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Represents a one-to-one dependency between partitions of the parent and child RDDs.
  */
-@DeveloperAPI
+@DeveloperApi
 class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
   override def getParents(partitionId: Int) = List(partitionId)
 }
 
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Represents a one-to-one dependency between ranges of partitions in the parent and child RDDs.
  * @param rdd the parent RDD
  * @param inStart the start of the range in the parent RDD
  * @param outStart the start of the range in the child RDD
  * @param length the length of the range
  */
-@DeveloperAPI
+@DeveloperApi
 class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int)
   extends NarrowDependency[T](rdd) {
 

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

@@ -21,18 +21,18 @@ import org.apache.log4j.{LogManager, PropertyConfigurator}
 import org.slf4j.{Logger, LoggerFactory}
 import org.slf4j.impl.StaticLoggerBinder
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Utility trait for classes that want to log data. Creates a SLF4J logger for the class and allows
  * logging messages at different levels using methods that only evaluate parameters lazily if the
  * log level is enabled.
  * 
  * NOTE: DO NOT USE this class outside of Spark. It is intended as an internal utility.
  *       This will likely be changed or removed in future releases.
  */
-@DeveloperAPI
+@DeveloperApi
 trait Logging {
   // Make the log field transient so that objects with Logging can
   // be serialized and used on another machine

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

@@ -23,9 +23,9 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.ObjectWritable
 import org.apache.hadoop.io.Writable
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 
-@DeveloperAPI
+@DeveloperApi
 class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable {
   def value = t
   override def toString = t.toString

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

@@ -35,7 +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.annotations.{DeveloperAPI, Experimental}
+import org.apache.spark.annotations.{DeveloperApi, Experimental}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
 import org.apache.spark.partial.{ApproximateEvaluator, PartialResult}
@@ -49,15 +49,15 @@ import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedHashMap, Utils}
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
  * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.
  *
  * @param config a Spark Config object describing the application configuration. Any settings in
  *   this config overrides the default configs as well as system properties.
  */
 
-@DeveloperAPI
+@DeveloperApi
 class SparkContext(config: SparkConf) extends Logging {
 
   // This is used only by YARN for now, but should be relevant to other cluster types (Mesos,
@@ -66,14 +66,14 @@ class SparkContext(config: SparkConf) extends Logging {
   private[spark] var preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()
 
   /**
-   * :: DeveloperAPI ::
+   * :: DeveloperApi ::
    * Alternative constructor for setting preferred locations where Spark will create executors.
    *
    * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Ca
    * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
    */
-    @DeveloperAPI
+    @DeveloperApi
     def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
       this(config)
       this.preferredNodeLocationData = preferredNodeLocationData
@@ -718,10 +718,10 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * :: DeveloperAPI ::
+   * :: DeveloperApi ::
    * Register a listener to receive up-calls from events that happen during execution.
    */
-  @DeveloperAPI
+  @DeveloperApi
   def addSparkListener(listener: SparkListener) {
     listenerBus.addListener(listener)
   }
@@ -1031,10 +1031,10 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * :: DeveloperAPI ::
+   * :: DeveloperApi ::
    * Run a job that can return approximate results.
    */
-  @DeveloperAPI
+  @DeveloperApi
   def runApproximateJob[T, U, R](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,

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

@@ -25,7 +25,7 @@ import scala.util.Properties
 import akka.actor._
 import com.google.common.collect.MapMaker
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 import org.apache.spark.api.python.PythonWorkerFactory
 import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
@@ -36,14 +36,14 @@ import org.apache.spark.storage._
 import org.apache.spark.util.{AkkaUtils, Utils}
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Holds all the runtime environment objects for a running Spark instance (either master or worker),
  * including the serializer, Akka actor system, block manager, map output tracker, etc. Currently
  * Spark code finds the SparkEnv through a thread-local variable, so each thread that accesses these
  * objects needs to have the right SparkEnv set. You can get the current environment with
  * SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set.
  */
-@DeveloperAPI
+@DeveloperApi
 class SparkEnv (
     val executorId: String,
     val actorSystem: ActorSystem,

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

@@ -19,14 +19,14 @@ package org.apache.spark
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Contextual information about a task which can be read or mutated during execution.
  */
-@DeveloperAPI
+@DeveloperApi
 class TaskContext(
   val stageId: Int,
   val partitionId: Int,

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

@@ -17,34 +17,34 @@
 
 package org.apache.spark
 
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * Various possible reasons why a task ended. The low-level TaskScheduler is supposed to retry
  * tasks several times for "ephemeral" failures, and only report back failures that require some
  * old stages to be resubmitted, such as shuffle map fetch failures.
  */
-@DeveloperAPI
+@DeveloperApi
 sealed trait TaskEndReason
 
-@DeveloperAPI
+@DeveloperApi
 case object Success extends TaskEndReason
 
-@DeveloperAPI
+@DeveloperApi
 case object Resubmitted extends TaskEndReason // Task was finished earlier but we've now lost it
 
-@DeveloperAPI
+@DeveloperApi
 case class FetchFailed(
     bmAddress: BlockManagerId,
     shuffleId: Int,
     mapId: Int,
     reduceId: Int)
   extends TaskEndReason
 
-@DeveloperAPI
+@DeveloperApi
 case class ExceptionFailure(
     className: String,
     description: String,
@@ -53,28 +53,28 @@ case class ExceptionFailure(
   extends TaskEndReason
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * The task finished successfully, but the result was lost from the executor's block manager before
  * it was fetched.
  */
-@DeveloperAPI
+@DeveloperApi
 case object TaskResultLost extends TaskEndReason
 
-@DeveloperAPI
+@DeveloperApi
 case object TaskKilled extends TaskEndReason
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * The task failed because the executor that it was running on was lost. This may happen because
  * the task crashed the JVM.
  */
-@DeveloperAPI
+@DeveloperApi
 case object ExecutorLostFailure extends TaskEndReason
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * We don't know why the task ended -- for example, because of a ClassNotFound exception when
  * deserializing the task result.
  */
-@DeveloperAPI
+@DeveloperApi
 case object UnknownReason extends TaskEndReason

core/src/main/scala/org/apache/spark/annotations/DeveloperAPI.java renamed to core/src/main/scala/org/apache/spark/annotations/DeveloperApi.java

@@ -22,4 +22,4 @@
 @Retention(RetentionPolicy.SOURCE)
 @Target({ElementType.TYPE, ElementType.FIELD, ElementType.METHOD,
         ElementType.CONSTRUCTOR, ElementType.LOCAL_VARIABLE, ElementType.PACKAGE})
-public @interface DeveloperAPI {}
+public @interface DeveloperApi {}

core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala

@@ -19,16 +19,16 @@ package org.apache.spark.broadcast
 
 import org.apache.spark.SecurityManager
 import org.apache.spark.SparkConf
-import org.apache.spark.annotations.DeveloperAPI
+import org.apache.spark.annotations.DeveloperApi
 
 /**
- * :: DeveloperAPI ::
+ * :: DeveloperApi ::
  * An interface for all the broadcast implementations in Spark (to allow
  * multiple broadcast implementations). SparkContext uses a user-specified
  * BroadcastFactory implementation to instantiate a particular broadcast for the
  * entire Spark job.
  */
-@DeveloperAPI
+@DeveloperApi
 trait BroadcastFactory {
   def initialize(isDriver: Boolean, conf: SparkConf,  securityMgr: SecurityManager): Unit
   def newBroadcast[T](value: T, isLocal: Boolean, id: Long): Broadcast[T]