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Remove dead scaladoc links

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Sean Owen
Sean Owen committed Feb 27, 2014
1 parent b8ff8cb commit 007762ba2ffa9c3b396de95e64652935a07e74e0
@@ -27,7 +27,7 @@ object Bagel extends Logging {
/**
* Runs a Bagel program.
- * @param sc [[org.apache.spark.SparkContext]] to use for the program.
+ * @param sc org.apache.spark.SparkContext to use for the program.
* @param vertices vertices of the graph represented as an RDD of (Key, Vertex) pairs. Often the
* Key will be the vertex id.
* @param messages initial set of messages represented as an RDD of (Key, Message) pairs. Often
@@ -38,12 +38,12 @@ object Bagel extends Logging {
* @param aggregator [[org.apache.spark.bagel.Aggregator]] performs a reduce across all vertices
* after each superstep and provides the result to each vertex in the next
* superstep.
- * @param partitioner [[org.apache.spark.Partitioner]] partitions values by key
+ * @param partitioner org.apache.spark.Partitioner partitions values by key
* @param numPartitions number of partitions across which to split the graph.
* Default is the default parallelism of the SparkContext
- * @param storageLevel [[org.apache.spark.storage.StorageLevel]] to use for caching of
+ * @param storageLevel org.apache.spark.storage.StorageLevel to use for caching of
* intermediate RDDs in each superstep. Defaults to caching in memory.
- * @param compute function that takes a Vertex, optional set of (possibly combined) messages to
+ *@param compute function that takes a Vertex, optional set of (possibly combined) messages to
* the Vertex, optional Aggregator and the current superstep,
* and returns a set of (Vertex, outgoing Messages) pairs
* @tparam K key
@@ -131,7 +131,7 @@ object Bagel extends Logging {
/**
* Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]], default
- * [[org.apache.spark.HashPartitioner]] and default storage level
+ * org.apache.spark.HashPartitioner and default storage level
*/
def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest, C: Manifest](
sc: SparkContext,
@@ -146,7 +146,7 @@ object Bagel extends Logging {
/**
* Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]] and the
- * default [[org.apache.spark.HashPartitioner]]
+ * default org.apache.spark.HashPartitioner
*/
def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest, C: Manifest](
sc: SparkContext,
@@ -166,7 +166,7 @@ object Bagel extends Logging {
/**
* Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]],
- * default [[org.apache.spark.HashPartitioner]],
+ * default org.apache.spark.HashPartitioner,
* [[org.apache.spark.bagel.DefaultCombiner]] and the default storage level
*/
def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest](
@@ -180,7 +180,7 @@ object Bagel extends Logging {
/**
* Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]],
- * the default [[org.apache.spark.HashPartitioner]]
+ * the default org.apache.spark.HashPartitioner
* and [[org.apache.spark.bagel.DefaultCombiner]]
*/
def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest](
@@ -351,7 +351,7 @@ class SparkContext(
* using the older MapReduce API (`org.apache.hadoop.mapred`).
*
* @param conf JobConf for setting up the dataset
- * @param inputFormatClass Class of the [[InputFormat]]
+ * @param inputFormatClass Class of the InputFormat
* @param keyClass Class of the keys
* @param valueClass Class of the values
* @param minSplits Minimum number of Hadoop Splits to generate.
@@ -23,9 +23,9 @@ import scala.util.control.{ControlThrowable, NonFatal}
import com.typesafe.config.Config
/**
- * An [[akka.actor.ActorSystem]] which refuses to shut down in the event of a fatal exception.
+ * An akka.actor.ActorSystem which refuses to shut down in the event of a fatal exception
* This is necessary as Spark Executors are allowed to recover from fatal exceptions
- * (see [[org.apache.spark.executor.Executor]]).
+ * (see org.apache.spark.executor.Executor)
*/
object IndestructibleActorSystem {
def apply(name: String, config: Config): ActorSystem =
@@ -20,8 +20,7 @@ package org.apache.spark.util
/**
* A class for tracking the statistics of a set of numbers (count, mean and variance) in a
* numerically robust way. Includes support for merging two StatCounters. Based on
- * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
- * Welford and Chan's algorithms for running variance]].
+ * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance Welford and Chan's algorithms for running variance]].
*
* @constructor Initialize the StatCounter with the given values.
*/
@@ -136,7 +136,7 @@ object Vector {
/**
* Creates this [[org.apache.spark.util.Vector]] of given length containing random numbers
- * between 0.0 and 1.0. Optional [[scala.util.Random]] number generator can be provided.
+ * between 0.0 and 1.0. Optional scala.util.Random number generator can be provided.
*/
def random(length: Int, random: Random = new XORShiftRandom()) =
Vector(length, _ => random.nextDouble())
@@ -127,7 +127,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new DStream by applying `groupByKey` on each RDD of `this` DStream.
* Therefore, the values for each key in `this` DStream's RDDs are grouped into a
- * single sequence to generate the RDDs of the new DStream. [[org.apache.spark.Partitioner]]
+ * single sequence to generate the RDDs of the new DStream. org.apache.spark.Partitioner
* is used to control the partitioning of each RDD.
*/
def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JList[V]] =
@@ -151,7 +151,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
- * merged using the supplied reduce function. [[org.apache.spark.Partitioner]] is used to control
+ * merged using the supplied reduce function. org.apache.spark.Partitioner is used to control
* thepartitioning of each RDD.
*/
def reduceByKey(func: JFunction2[V, V, V], partitioner: Partitioner): JavaPairDStream[K, V] = {
@@ -161,7 +161,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Combine elements of each key in DStream's RDDs using custom function. This is similar to the
* combineByKey for RDDs. Please refer to combineByKey in
- * [[org.apache.spark.rdd.PairRDDFunctions]] for more information.
+ * org.apache.spark.rdd.PairRDDFunctions for more information.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
@@ -176,7 +176,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Combine elements of each key in DStream's RDDs using custom function. This is similar to the
* combineByKey for RDDs. Please refer to combineByKey in
- * [[org.apache.spark.rdd.PairRDDFunctions]] for more information.
+ * org.apache.spark.rdd.PairRDDFunctions for more information.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
@@ -479,7 +479,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of the key.
- * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
* @param partitioner Partitioner for controlling the partitioning of each RDD in the new
@@ -579,7 +579,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
- * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
*/
def join[W](
other: JavaPairDStream[K, W],
@@ -619,7 +619,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
- * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
*/
def leftOuterJoin[W](
other: JavaPairDStream[K, W],
@@ -660,7 +660,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
/**
* Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and
- * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+ * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
* the partitioning of each RDD.
*/
def rightOuterJoin[W](
@@ -406,7 +406,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
* JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using
* [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream().
* In the transform function, convert the JavaRDD corresponding to that JavaDStream to
- * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD().
+ * a JavaPairRDD using org.apache.spark.api.java.JavaPairRDD.fromJavaRDD().
*/
def transform[T](
dstreams: JList[JavaDStream[_]],
@@ -429,7 +429,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
* JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using
* [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream().
* In the transform function, convert the JavaRDD corresponding to that JavaDStream to
- * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD().
+ * a JavaPairRDD using org.apache.spark.api.java.JavaPairRDD.fromJavaRDD().
*/
def transform[K, V](
dstreams: JList[JavaDStream[_]],
@@ -65,7 +65,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying `groupByKey` on each RDD. The supplied
- * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
*/
def groupByKey(partitioner: Partitioner): DStream[(K, Seq[V])] = {
val createCombiner = (v: V) => ArrayBuffer[V](v)
@@ -95,7 +95,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
- * merged using the supplied reduce function. [[org.apache.spark.Partitioner]] is used to control
+ * merged using the supplied reduce function. org.apache.spark.Partitioner is used to control
* the partitioning of each RDD.
*/
def reduceByKey(reduceFunc: (V, V) => V, partitioner: Partitioner): DStream[(K, V)] = {
@@ -376,7 +376,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of the key.
- * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
* @param partitioner Partitioner for controlling the partitioning of each RDD in the new
@@ -396,7 +396,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
- * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated. Note, that
* this function may generate a different a tuple with a different key
@@ -453,7 +453,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream.
- * The supplied [[org.apache.spark.Partitioner]] is used to partition the generated RDDs.
+ * The supplied org.apache.spark.Partitioner is used to partition the generated RDDs.
*/
def cogroup[W: ClassTag](
other: DStream[(K, W)],
@@ -483,7 +483,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
- * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+ * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
*/
def join[W: ClassTag](
other: DStream[(K, W)],
@@ -518,7 +518,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and
- * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+ * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
* the partitioning of each RDD.
*/
def leftOuterJoin[W: ClassTag](
@@ -554,7 +554,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
/**
* Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and
- * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+ * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
* the partitioning of each RDD.
*/
def rightOuterJoin[W: ClassTag](

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