[SPARK-3359][DOCS] Make javadoc8 working for unidoc/genjavadoc compatibility in Java API documentation

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

@@ -26,7 +26,7 @@ package org.apache.spark
  *
  * An accumulator is created from an initial value `v` by calling
  * [[SparkContext#accumulator SparkContext.accumulator]].
- * Tasks running on the cluster can then add to it using the [[Accumulable#+= +=]] operator.
+ * Tasks running on the cluster can then add to it using the `+=` operator.
  * However, they cannot read its value. Only the driver program can read the accumulator's value,
  * using its [[#value]] method.
  *

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

@@ -262,7 +262,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then seconds are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getTimeAsSeconds(key: String): Long = {
     Utils.timeStringAsSeconds(get(key))
@@ -279,7 +279,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then milliseconds are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getTimeAsMs(key: String): Long = {
     Utils.timeStringAsMs(get(key))
@@ -296,7 +296,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a size parameter as bytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then bytes are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getSizeAsBytes(key: String): Long = {
     Utils.byteStringAsBytes(get(key))
@@ -320,7 +320,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then Kibibytes are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getSizeAsKb(key: String): Long = {
     Utils.byteStringAsKb(get(key))
@@ -337,7 +337,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then Mebibytes are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getSizeAsMb(key: String): Long = {
     Utils.byteStringAsMb(get(key))
@@ -354,7 +354,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
   /**
    * Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then Gibibytes are assumed.
-   * @throws NoSuchElementException
+   * @throws java.util.NoSuchElementException
    */
   def getSizeAsGb(key: String): Long = {
     Utils.byteStringAsGb(get(key))

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

@@ -645,7 +645,7 @@ class SparkContext(config: SparkConf) extends Logging {
 
   /**
    * Get a local property set in this thread, or null if it is missing. See
-   * [[org.apache.spark.SparkContext.setLocalProperty]].
+   * `org.apache.spark.SparkContext.setLocalProperty`.
    */
   def getLocalProperty(key: String): String =
     Option(localProperties.get).map(_.getProperty(key)).orNull
@@ -663,7 +663,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * Application programmers can use this method to group all those jobs together and give a
    * group description. Once set, the Spark web UI will associate such jobs with this group.
    *
-   * The application can also use [[org.apache.spark.SparkContext.cancelJobGroup]] to cancel all
+   * The application can also use `org.apache.spark.SparkContext.cancelJobGroup` to cancel all
    * running jobs in this group. For example,
    * {{{
    * // In the main thread:
@@ -1384,7 +1384,7 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * Create and register a [[CollectionAccumulator]], which starts with empty list and accumulates
+   * Create and register a `CollectionAccumulator`, which starts with empty list and accumulates
    * inputs by adding them into the list.
    */
   def collectionAccumulator[T]: CollectionAccumulator[T] = {
@@ -1394,7 +1394,7 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * Create and register a [[CollectionAccumulator]], which starts with empty list and accumulates
+   * Create and register a `CollectionAccumulator`, which starts with empty list and accumulates
    * inputs by adding them into the list.
    */
   def collectionAccumulator[T](name: String): CollectionAccumulator[T] = {
@@ -2043,7 +2043,7 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /**
-   * Cancel active jobs for the specified group. See [[org.apache.spark.SparkContext.setJobGroup]]
+   * Cancel active jobs for the specified group. See `org.apache.spark.SparkContext.setJobGroup`
    * for more information.
    */
   def cancelJobGroup(groupId: String) {
@@ -2061,7 +2061,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * Cancel a given job if it's scheduled or running.
    *
    * @param jobId the job ID to cancel
-   * @throws InterruptedException if the cancel message cannot be sent
+   * @note Throws `InterruptedException` if the cancel message cannot be sent
    */
   def cancelJob(jobId: Int) {
     dagScheduler.cancelJob(jobId)
@@ -2071,7 +2071,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * Cancel a given stage and all jobs associated with it.
    *
    * @param stageId the stage ID to cancel
-   * @throws InterruptedException if the cancel message cannot be sent
+   * @note Throws `InterruptedException` if the cancel message cannot be sent
    */
   def cancelStage(stageId: Int) {
     dagScheduler.cancelStage(stageId)

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

@@ -164,7 +164,7 @@ abstract class TaskContext extends Serializable {
 
   /**
    * Get a local property set upstream in the driver, or null if it is missing. See also
-   * [[org.apache.spark.SparkContext.setLocalProperty]].
+   * `org.apache.spark.SparkContext.setLocalProperty`.
    */
   def getLocalProperty(key: String): String
 
@@ -174,7 +174,7 @@ abstract class TaskContext extends Serializable {
   /**
    * ::DeveloperApi::
    * Returns all metrics sources with the given name which are associated with the instance
-   * which runs the task. For more information see [[org.apache.spark.metrics.MetricsSystem!]].
+   * which runs the task. For more information see `org.apache.spark.metrics.MetricsSystem`.
    */
   @DeveloperApi
   def getMetricsSources(sourceName: String): Seq[Source]

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

@@ -65,7 +65,7 @@ sealed trait TaskFailedReason extends TaskEndReason {
 
 /**
  * :: DeveloperApi ::
- * A [[org.apache.spark.scheduler.ShuffleMapTask]] that completed successfully earlier, but we
+ * A `org.apache.spark.scheduler.ShuffleMapTask` that completed successfully earlier, but we
  * lost the executor before the stage completed. This means Spark needs to reschedule the task
  * to be re-executed on a different executor.
  */

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

@@ -186,7 +186,7 @@ private[spark] object TestUtils {
 
 
 /**
- * A [[SparkListener]] that detects whether spills have occurred in Spark jobs.
+ * A `SparkListener` that detects whether spills have occurred in Spark jobs.
  */
 private class SpillListener extends SparkListener {
   private val stageIdToTaskMetrics = new mutable.HashMap[Int, ArrayBuffer[TaskMetrics]]

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

@@ -103,7 +103,8 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
    * @param withReplacement can elements be sampled multiple times (replaced when sampled out)
    * @param fraction expected size of the sample as a fraction of this RDD's size
    *  without replacement: probability that each element is chosen; fraction must be [0, 1]
-   *  with replacement: expected number of times each element is chosen; fraction must be >= 0
+   *  with replacement: expected number of times each element is chosen; fraction must be greater
+   *  than or equal to 0
    *
    * @note This is NOT guaranteed to provide exactly the fraction of the count
    * of the given `RDD`.
@@ -117,7 +118,8 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
    * @param withReplacement can elements be sampled multiple times (replaced when sampled out)
    * @param fraction expected size of the sample as a fraction of this RDD's size
    *  without replacement: probability that each element is chosen; fraction must be [0, 1]
-   *  with replacement: expected number of times each element is chosen; fraction must be >= 0
+   *  with replacement: expected number of times each element is chosen; fraction must be greater
+   *  than or equal to 0
    * @param seed seed for the random number generator
    *
    * @note This is NOT guaranteed to provide exactly the fraction of the count
@@ -167,7 +169,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T])
    * Return an RDD with the elements from `this` that are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be less than or equal to us.
    */
   def subtract(other: JavaRDD[T]): JavaRDD[T] = wrapRDD(rdd.subtract(other))
 

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

@@ -152,10 +152,10 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
 
   /**
    * Compute a histogram using the provided buckets. The buckets are all open
-   * to the right except for the last which is closed
+   * to the right except for the last which is closed.
    *  e.g. for the array
    *  [1, 10, 20, 50] the buckets are [1, 10) [10, 20) [20, 50]
-   *  e.g 1<=x<10 , 10<=x<20, 20<=x<=50
+   *  e.g {@code <=x<10, 10<=x<20, 20<=x<=50}
    *  And on the input of 1 and 50 we would have a histogram of 1, 0, 1
    *
    * @note If your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched

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

@@ -96,7 +96,7 @@ private[spark] class HadoopPartition(rddId: Int, override val index: Int, s: Inp
  * @param minPartitions Minimum number of HadoopRDD partitions (Hadoop Splits) to generate.
  *
  * @note Instantiating this class directly is not recommended, please use
- * [[org.apache.spark.SparkContext.hadoopRDD()]]
+ * `org.apache.spark.SparkContext.hadoopRDD()`
  */
 @DeveloperApi
 class HadoopRDD[K, V](

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

@@ -41,7 +41,10 @@ private[spark] class JdbcPartition(idx: Int, val lower: Long, val upper: Long) e
  *   The RDD takes care of closing the connection.
  * @param sql the text of the query.
  *   The query must contain two ? placeholders for parameters used to partition the results.
- *   E.g. "select title, author from books where ? <= id and id <= ?"
+ *   For example,
+ *   {{{
+ *   select title, author from books where ? <= id and id <= ?
+ *   }}}
  * @param lowerBound the minimum value of the first placeholder
  * @param upperBound the maximum value of the second placeholder
  *   The lower and upper bounds are inclusive.
@@ -151,7 +154,10 @@ object JdbcRDD {
    *   The RDD takes care of closing the connection.
    * @param sql the text of the query.
    *   The query must contain two ? placeholders for parameters used to partition the results.
-   *   E.g. "select title, author from books where ? <= id and id <= ?"
+   *   For example,
+   *   {{{
+   *   select title, author from books where ? <= id and id <= ?
+   *   }}}
    * @param lowerBound the minimum value of the first placeholder
    * @param upperBound the maximum value of the second placeholder
    *   The lower and upper bounds are inclusive.
@@ -191,7 +197,10 @@ object JdbcRDD {
    *   The RDD takes care of closing the connection.
    * @param sql the text of the query.
    *   The query must contain two ? placeholders for parameters used to partition the results.
-   *   E.g. "select title, author from books where ? <= id and id <= ?"
+   *   For example,
+   *   {{{
+   *   select title, author from books where ? <= id and id <= ?
+   *   }}}
    * @param lowerBound the minimum value of the first placeholder
    * @param upperBound the maximum value of the second placeholder
    *   The lower and upper bounds are inclusive.

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

@@ -63,7 +63,7 @@ private[spark] class NewHadoopPartition(
  * @param valueClass Class of the value associated with the inputFormatClass.
  *
  * @note Instantiating this class directly is not recommended, please use
- * [[org.apache.spark.SparkContext.newAPIHadoopRDD()]]
+ * `org.apache.spark.SparkContext.newAPIHadoopRDD()`
  */
 @DeveloperApi
 class NewHadoopRDD[K, V](

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

@@ -399,9 +399,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
    * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
    *
-   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero `sp > p`
-   * would trigger sparse representation of registers, which may reduce the memory consumption
-   * and increase accuracy when the cardinality is small.
+   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero (`sp` is
+   * greater than `p`) would trigger sparse representation of registers, which may reduce the
+   * memory consumption and increase accuracy when the cardinality is small.
    *
    * @param p The precision value for the normal set.
    *          `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).
@@ -492,8 +492,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * each time the resulting RDD is evaluated.
    *
    * @note This operation may be very expensive. If you are grouping in order to perform an
-   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
-   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
+   * aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
+   * or `PairRDDFunctions.reduceByKey` will provide much better performance.
    *
    * @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
    * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
@@ -516,8 +516,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
    *
    * @note This operation may be very expensive. If you are grouping in order to perform an
-   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
-   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
+   * aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
+   * or `PairRDDFunctions.reduceByKey` will provide much better performance.
    *
    * @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
    * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
@@ -637,8 +637,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * evaluated.
    *
    * @note This operation may be very expensive. If you are grouping in order to perform an
-   * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
-   * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
+   * aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
+   * or `PairRDDFunctions.reduceByKey` will provide much better performance.
    */
   def groupByKey(): RDD[(K, Iterable[V])] = self.withScope {
     groupByKey(defaultPartitioner(self))
@@ -908,7 +908,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Return an RDD with the pairs from `this` whose keys are not in `other`.
    *
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
-   * RDD will be <= us.
+   * RDD will be less than or equal to us.
    */
   def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope {
     subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length)))

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

@@ -469,7 +469,8 @@ abstract class RDD[T: ClassTag](
    * @param withReplacement can elements be sampled multiple times (replaced when sampled out)
    * @param fraction expected size of the sample as a fraction of this RDD's size
    *  without replacement: probability that each element is chosen; fraction must be [0, 1]
-   *  with replacement: expected number of times each element is chosen; fraction must be >= 0
+   *  with replacement: expected number of times each element is chosen; fraction must be greater
+   *  than or equal to 0
    * @param seed seed for the random number generator
    *
    * @note This is NOT guaranteed to provide exactly the fraction of the count
@@ -750,8 +751,10 @@ abstract class RDD[T: ClassTag](
    *                        print line function (like out.println()) as the 2nd parameter.
    *                        An example of pipe the RDD data of groupBy() in a streaming way,
    *                        instead of constructing a huge String to concat all the elements:
-   *                        def printRDDElement(record:(String, Seq[String]), f:String=>Unit) =
-   *                          for (e <- record._2) {f(e)}
+   *                        {{{
+   *                        def printRDDElement(record:(String, Seq[String]), f:String=>Unit) =
+   *                          for (e <- record._2) {f(e)}
+   *                        }}}
    * @param separateWorkingDir Use separate working directories for each task.
    * @param bufferSize Buffer size for the stdin writer for the piped process.
    * @param encoding Char encoding used for interacting (via stdin, stdout and stderr) with
@@ -1184,8 +1187,13 @@ abstract class RDD[T: ClassTag](
    *
    * @note This method should only be used if the resulting map is expected to be small, as
    * the whole thing is loaded into the driver's memory.
-   * To handle very large results, consider using rdd.map(x =&gt; (x, 1L)).reduceByKey(_ + _), which
-   * returns an RDD[T, Long] instead of a map.
+   * To handle very large results, consider using
+   *
+   * {{{
+   * rdd.map(x => (x, 1L)).reduceByKey(_ + _)
+   * }}}
+   *
+   * , which returns an RDD[T, Long] instead of a map.
    */
   def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope {
     map(value => (value, null)).countByKey()
@@ -1223,9 +1231,9 @@ abstract class RDD[T: ClassTag](
    * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
    * <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
    *
-   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero `sp &gt; p`
-   * would trigger sparse representation of registers, which may reduce the memory consumption
-   * and increase accuracy when the cardinality is small.
+   * The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero (`sp` is greater
+   * than `p`) would trigger sparse representation of registers, which may reduce the memory
+   * consumption and increase accuracy when the cardinality is small.
    *
    * @param p The precision value for the normal set.
    *          `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).

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

@@ -23,7 +23,8 @@ import org.apache.spark.Partition
 
 /**
  * Enumeration to manage state transitions of an RDD through checkpointing
- * [ Initialized --> checkpointing in progress --> checkpointed ].
+ *
+ * [ Initialized --{@literal >} checkpointing in progress --{@literal >} checkpointed ]
  */
 private[spark] object CheckpointState extends Enumeration {
   type CheckpointState = Value