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Author: pwendell

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

@@ -20,7 +20,7 @@ package org.apache.spark
 import org.apache.spark.util.collection.{AppendOnlyMap, ExternalAppendOnlyMap}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * A set of functions used to aggregate data.
  *
  * @param createCombiner function to create the initial value of the aggregation.

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

@@ -21,14 +21,14 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Base class for dependencies.
  */
 abstract class Dependency[T](val rdd: RDD[T]) extends Serializable
 
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * 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.
  */
@@ -43,7 +43,7 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {
 
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Represents a dependency on the output of a shuffle stage.
  * @param rdd the parent RDD
  * @param partitioner partitioner used to partition the shuffle output
@@ -62,7 +62,7 @@ class ShuffleDependency[K, V](
 
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Represents a one-to-one dependency between partitions of the parent and child RDDs.
  */
 class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
@@ -71,7 +71,7 @@ class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
 
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * 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

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

@@ -25,7 +25,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.{JobFailed, JobSucceeded, JobWaiter}
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * <span class="experimental badge">Experimental</span>
  * A future for the result of an action to support cancellation. This is an extension of the
  * Scala Future interface to support cancellation.
  */
@@ -85,7 +85,7 @@ trait FutureAction[T] extends Future[T] {
 
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * <span class="experimental badge">Experimental</span>
  * A [[FutureAction]] holding the result of an action that triggers a single job. Examples include
  * count, collect, reduce.
  */
@@ -150,7 +150,7 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc:
 
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * <span class="experimental badge">Experimental</span>
  * A [[FutureAction]] for actions that could trigger multiple Spark jobs. Examples include take,
  * takeSample. Cancellation works by setting the cancelled flag to true and interrupting the
  * action thread if it is being blocked by a job.

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

@@ -22,7 +22,7 @@ import org.slf4j.{Logger, LoggerFactory}
 import org.slf4j.impl.StaticLoggerBinder
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * 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.

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

@@ -23,7 +23,7 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.ObjectWritable
 import org.apache.hadoop.io.Writable
 
-/** <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span> */
+/** <span class="developer badge">Developer API</span> */
 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

@@ -63,13 +63,13 @@ class SparkContext(config: SparkConf)
   private[spark] var preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()
 
   /**
-   * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+   * <span class="developer badge">Developer API</span>
    * 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.
-   */
+
     def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
       this(config)
       this.preferredNodeLocationData = preferredNodeLocationData
@@ -714,7 +714,7 @@ class SparkContext(config: SparkConf)
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+   * <span class="developer badge">Developer API</span>
    * Register a listener to receive up-calls from events that happen during execution.
    */
   def addSparkListener(listener: SparkListener) {
@@ -1026,7 +1026,7 @@ class SparkContext(config: SparkConf)
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+   * <span class="developer badge">Developer API</span>
    * Run a job that can return approximate results.
    */
   def runApproximateJob[T, U, R](
@@ -1044,7 +1044,7 @@ class SparkContext(config: SparkConf)
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * <span class="experimental badge">Experimental</span>
    * Submit a job for execution and return a FutureJob holding the result.
    */
   def submitJob[T, U, R](

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

@@ -35,7 +35,7 @@ import org.apache.spark.storage._
 import org.apache.spark.util.{AkkaUtils, Utils}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * 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

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

@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import org.apache.spark.executor.TaskMetrics
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Contextual information about a task which can be read or mutated during execution.
  */
 class TaskContext(

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

@@ -21,7 +21,7 @@ import org.apache.spark.SecurityManager
 import org.apache.spark.SparkConf
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * 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

core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala

@@ -20,7 +20,7 @@ package org.apache.spark.executor
 import org.apache.spark.storage.{BlockId, BlockStatus}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Metrics tracked during the execution of a task.
  */
 class TaskMetrics extends Serializable {
@@ -87,7 +87,7 @@ object TaskMetrics {
 
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Metrics pertaining to shuffle data read in a given task.
  */
 class ShuffleReadMetrics extends Serializable {
@@ -125,7 +125,7 @@ class ShuffleReadMetrics extends Serializable {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * <span class="developer badge">Developer API</span>
  * Metrics pertaining to shuffle data written in a given task.
  */
 class ShuffleWriteMetrics extends Serializable {