[SPARK-3249][DOC] Fix links in ScalaDoc that cause warning messages in `sbt/sbt unidoc`

## What changes were proposed in this pull request?

This PR proposes to fix ambiguous link warnings by simply making them as code blocks for both javadoc and scaladoc.

```
[warn] .../spark/core/src/main/scala/org/apache/spark/Accumulator.scala:20: The link target "SparkContext#accumulator" is ambiguous. Several members fit the target:
[warn] .../spark/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala:281: The link target "runMiniBatchSGD" is ambiguous. Several members fit the target:
[warn] .../spark/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala:83: The link target "run" is ambiguous. Several members fit the target:
...
```

This PR also fixes javadoc8 break as below:

```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/LowPrioritySQLImplicits.java:7: error: reference not found
[error]  * newProductEncoder - to disambiguate for {link List}s which are both {link Seq} and {link Product}
[error]                                                   ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/LowPrioritySQLImplicits.java:7: error: reference not found
[error]  * newProductEncoder - to disambiguate for {link List}s which are both {link Seq} and {link Product}
[error]                                                                                ^
[error] .../spark/sql/core/target/java/org/apache/spark/sql/LowPrioritySQLImplicits.java:7: error: reference not found
[error]  * newProductEncoder - to disambiguate for {link List}s which are both {link Seq} and {link Product}
[error]                                                                                                ^
[info] 3 errors
```

## How was this patch tested?

Manually via `sbt unidoc > output.txt` and the checked it via `cat output.txt | grep ambiguous`

and `sbt unidoc | grep error`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16604 from HyukjinKwon/SPARK-3249.

5 files changed, 9 insertions, 10 deletions

diff --git a/core/src/main/scala/org/apache/spark/Accumulator.scala b/core/src/main/scala/org/apache/spark/Accumulator.scala
index 7bea636c94..9d5fbefc82 100644
--- a/core/src/main/scala/org/apache/spark/Accumulator.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulator.scala
@@ -24,8 +24,7 @@ package org.apache.spark
  * They can be used to implement counters (as in MapReduce) or sums. Spark natively supports
  * accumulators of numeric value types, and programmers can add support for new types.
  *
- * An accumulator is created from an initial value `v` by calling
- * [[SparkContext#accumulator SparkContext.accumulator]].
+ * An accumulator is created from an initial value `v` by calling `SparkContext.accumulator`.
  * 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.
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 766aea213a..9544475ff0 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -166,7 +166,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
    * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
    *
-   * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
+   * This method differs from `sampleByKey` in that we make additional passes over the RDD to
    * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
    * over all key values with a 99.99% confidence. When sampling without replacement, we need one
    * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
@@ -184,7 +184,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
    * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
    *
-   * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
+   * This method differs from `sampleByKey` in that we make additional passes over the RDD to
    * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
    * over all key values with a 99.99% confidence. When sampling without replacement, we need one
    * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index eda16d957c..91ae1002ab 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -393,7 +393,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def treeReduce(f: JFunction2[T, T, T], depth: Int): T = rdd.treeReduce(f, depth)
 
   /**
-   * [[org.apache.spark.api.java.JavaRDDLike#treeReduce]] with suggested depth 2.
+   * `org.apache.spark.api.java.JavaRDDLike.treeReduce` with suggested depth 2.
    */
   def treeReduce(f: JFunction2[T, T, T]): T = treeReduce(f, 2)
 
@@ -440,7 +440,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   }
 
   /**
-   * [[org.apache.spark.api.java.JavaRDDLike#treeAggregate]] with suggested depth 2.
+   * `org.apache.spark.api.java.JavaRDDLike.treeAggregate` with suggested depth 2.
    */
   def treeAggregate[U](
       zeroValue: U,
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index ec12b9963e..d7bfdbad84 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -109,7 +109,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * functions. This method is here for backward compatibility. It does not provide combiner
    * classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,
@@ -127,7 +127,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * This method is here for backward compatibility. It does not provide combiner
    * classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,
@@ -608,7 +608,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * existing partitioner/parallelism level. This method is here for backward compatibility. It
    * does not provide combiner classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 374abccf6a..a7e01f397e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1841,7 +1841,7 @@ abstract class RDD[T: ClassTag](
  * Defines implicit functions that provide extra functionalities on RDDs of specific types.
  *
  * For example, [[RDD.rddToPairRDDFunctions]] converts an RDD into a [[PairRDDFunctions]] for
- * key-value-pair RDDs, and enabling extra functionalities such as [[PairRDDFunctions.reduceByKey]].
+ * key-value-pair RDDs, and enabling extra functionalities such as `PairRDDFunctions.reduceByKey`.
  */
 object RDD {