[MINOR][DOC] In Dataset docs, remove self link to Dataset and add link to Column

## What changes were proposed in this pull request?
Documentation Fix

## How was this patch tested?

Author: Sandeep Singh <sandeep@techaddict.me>

Closes #13567 from techaddict/minor-4.

1 files changed, 100 insertions, 100 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index 6cbc27d91c..162524a9ef 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -67,7 +67,7 @@ private[sql] object Dataset {
 }
 
 /**
- * A [[Dataset]] is a strongly typed collection of domain-specific objects that can be transformed
+ * A Dataset is a strongly typed collection of domain-specific objects that can be transformed
  * in parallel using functional or relational operations. Each Dataset also has an untyped view
  * called a [[DataFrame]], which is a Dataset of [[Row]].
  *
@@ -105,7 +105,7 @@ private[sql] object Dataset {
  * }}}
  *
  * Dataset operations can also be untyped, through various domain-specific-language (DSL)
- * functions defined in: [[Dataset]] (this class), [[Column]], and [[functions]]. These operations
+ * functions defined in: Dataset (this class), [[Column]], and [[functions]]. These operations
  * are very similar to the operations available in the data frame abstraction in R or Python.
  *
  * To select a column from the Dataset, use `apply` method in Scala and `col` in Java.
@@ -194,13 +194,13 @@ class Dataset[T] private[sql](
   /**
    * Currently [[ExpressionEncoder]] is the only implementation of [[Encoder]], here we turn the
    * passed in encoder to [[ExpressionEncoder]] explicitly, and mark it implicit so that we can use
-   * it when constructing new [[Dataset]] objects that have the same object type (that will be
+   * it when constructing new Dataset objects that have the same object type (that will be
    * possibly resolved to a different schema).
    */
   private[sql] implicit val exprEnc: ExpressionEncoder[T] = encoderFor(encoder)
 
   /**
-   * Encoder is used mostly as a container of serde expressions in [[Dataset]].  We build logical
+   * Encoder is used mostly as a container of serde expressions in Dataset.  We build logical
    * plans by these serde expressions and execute it within the query framework.  However, for
    * performance reasons we may want to use encoder as a function to deserialize internal rows to
    * custom objects, e.g. collect.  Here we resolve and bind the encoder so that we can call its
@@ -340,7 +340,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] where each record has been mapped on to the specified type. The
+   * Returns a new Dataset where each record has been mapped on to the specified type. The
    * method used to map columns depend on the type of `U`:
    *  - When `U` is a class, fields for the class will be mapped to columns of the same name
    *    (case sensitivity is determined by `spark.sql.caseSensitive`).
@@ -349,7 +349,7 @@ class Dataset[T] private[sql](
    *  - When `U` is a primitive type (i.e. String, Int, etc), then the first column of the
    *    [[DataFrame]] will be used.
    *
-   * If the schema of the [[Dataset]] does not match the desired `U` type, you can use `select`
+   * If the schema of the Dataset does not match the desired `U` type, you can use `select`
    * along with `alias` or `as` to rearrange or rename as required.
    *
    * @group basic
@@ -385,7 +385,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns the schema of this [[Dataset]].
+   * Returns the schema of this Dataset.
    *
    * @group basic
    * @since 1.6.0
@@ -453,8 +453,8 @@ class Dataset[T] private[sql](
   def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation]
 
   /**
-   * Returns true if this [[Dataset]] contains one or more sources that continuously
-   * return data as it arrives. A [[Dataset]] that reads data from a streaming source
+   * Returns true if this Dataset contains one or more sources that continuously
+   * return data as it arrives. A Dataset that reads data from a streaming source
    * must be executed as a [[ContinuousQuery]] using the `startStream()` method in
    * [[DataFrameWriter]]. Methods that return a single answer, e.g. `count()` or
    * `collect()`, will throw an [[AnalysisException]] when there is a streaming
@@ -467,7 +467,7 @@ class Dataset[T] private[sql](
   def isStreaming: Boolean = logicalPlan.isStreaming
 
   /**
-   * Displays the [[Dataset]] in a tabular form. Strings more than 20 characters will be truncated,
+   * Displays the Dataset in a tabular form. Strings more than 20 characters will be truncated,
    * and all cells will be aligned right. For example:
    * {{{
    *   year  month AVG('Adj Close) MAX('Adj Close)
@@ -486,7 +486,7 @@ class Dataset[T] private[sql](
   def show(numRows: Int): Unit = show(numRows, truncate = true)
 
   /**
-   * Displays the top 20 rows of [[Dataset]] in a tabular form. Strings more than 20 characters
+   * Displays the top 20 rows of Dataset in a tabular form. Strings more than 20 characters
    * will be truncated, and all cells will be aligned right.
    *
    * @group action
@@ -495,7 +495,7 @@ class Dataset[T] private[sql](
   def show(): Unit = show(20)
 
   /**
-   * Displays the top 20 rows of [[Dataset]] in a tabular form.
+   * Displays the top 20 rows of Dataset in a tabular form.
    *
    * @param truncate Whether truncate long strings. If true, strings more than 20 characters will
    *                 be truncated and all cells will be aligned right
@@ -506,7 +506,7 @@ class Dataset[T] private[sql](
   def show(truncate: Boolean): Unit = show(20, truncate)
 
   /**
-   * Displays the [[Dataset]] in a tabular form. For example:
+   * Displays the Dataset in a tabular form. For example:
    * {{{
    *   year  month AVG('Adj Close) MAX('Adj Close)
    *   1980  12    0.503218        0.595103
@@ -727,7 +727,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Joins this [[Dataset]] returning a [[Tuple2]] for each pair where `condition` evaluates to
+   * Joins this Dataset returning a [[Tuple2]] for each pair where `condition` evaluates to
    * true.
    *
    * This is similar to the relation `join` function with one important difference in the
@@ -807,7 +807,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Using inner equi-join to join this [[Dataset]] returning a [[Tuple2]] for each pair
+   * Using inner equi-join to join this Dataset returning a [[Tuple2]] for each pair
    * where `condition` evaluates to true.
    *
    * @param other Right side of the join.
@@ -822,7 +822,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with each partition sorted by the given expressions.
+   * Returns a new Dataset with each partition sorted by the given expressions.
    *
    * This is the same operation as "SORT BY" in SQL (Hive QL).
    *
@@ -835,7 +835,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with each partition sorted by the given expressions.
+   * Returns a new Dataset with each partition sorted by the given expressions.
    *
    * This is the same operation as "SORT BY" in SQL (Hive QL).
    *
@@ -848,7 +848,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] sorted by the specified column, all in ascending order.
+   * Returns a new Dataset sorted by the specified column, all in ascending order.
    * {{{
    *   // The following 3 are equivalent
    *   ds.sort("sortcol")
@@ -865,7 +865,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] sorted by the given expressions. For example:
+   * Returns a new Dataset sorted by the given expressions. For example:
    * {{{
    *   ds.sort($"col1", $"col2".desc)
    * }}}
@@ -879,7 +879,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] sorted by the given expressions.
+   * Returns a new Dataset sorted by the given expressions.
    * This is an alias of the `sort` function.
    *
    * @group typedrel
@@ -889,7 +889,7 @@ class Dataset[T] private[sql](
   def orderBy(sortCol: String, sortCols: String*): Dataset[T] = sort(sortCol, sortCols : _*)
 
   /**
-   * Returns a new [[Dataset]] sorted by the given expressions.
+   * Returns a new Dataset sorted by the given expressions.
    * This is an alias of the `sort` function.
    *
    * @group typedrel
@@ -923,7 +923,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with an alias set.
+   * Returns a new Dataset with an alias set.
    *
    * @group typedrel
    * @since 1.6.0
@@ -933,7 +933,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * (Scala-specific) Returns a new [[Dataset]] with an alias set.
+   * (Scala-specific) Returns a new Dataset with an alias set.
    *
    * @group typedrel
    * @since 2.0.0
@@ -941,7 +941,7 @@ class Dataset[T] private[sql](
   def as(alias: Symbol): Dataset[T] = as(alias.name)
 
   /**
-   * Returns a new [[Dataset]] with an alias set. Same as `as`.
+   * Returns a new Dataset with an alias set. Same as `as`.
    *
    * @group typedrel
    * @since 2.0.0
@@ -949,7 +949,7 @@ class Dataset[T] private[sql](
   def alias(alias: String): Dataset[T] = as(alias)
 
   /**
-   * (Scala-specific) Returns a new [[Dataset]] with an alias set. Same as `as`.
+   * (Scala-specific) Returns a new Dataset with an alias set. Same as `as`.
    *
    * @group typedrel
    * @since 2.0.0
@@ -1008,7 +1008,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] by computing the given [[Column]] expression for each element.
+   * Returns a new Dataset by computing the given [[Column]] expression for each element.
    *
    * {{{
    *   val ds = Seq(1, 2, 3).toDS()
@@ -1045,7 +1045,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] by computing the given [[Column]] expressions for each element.
+   * Returns a new Dataset by computing the given [[Column]] expressions for each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1056,7 +1056,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] by computing the given [[Column]] expressions for each element.
+   * Returns a new Dataset by computing the given [[Column]] expressions for each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1070,7 +1070,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] by computing the given [[Column]] expressions for each element.
+   * Returns a new Dataset by computing the given [[Column]] expressions for each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1085,7 +1085,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Returns a new [[Dataset]] by computing the given [[Column]] expressions for each element.
+   * Returns a new Dataset by computing the given [[Column]] expressions for each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1154,7 +1154,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Groups the [[Dataset]] using the specified columns, so we can run aggregation on them. See
+   * Groups the Dataset using the specified columns, so we can run aggregation on them. See
    * [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
    * {{{
@@ -1177,7 +1177,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Create a multi-dimensional rollup for the current [[Dataset]] using the specified columns,
+   * Create a multi-dimensional rollup for the current Dataset using the specified columns,
    * so we can run aggregation on them.
    * See [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
@@ -1201,7 +1201,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Create a multi-dimensional cube for the current [[Dataset]] using the specified columns,
+   * Create a multi-dimensional cube for the current Dataset using the specified columns,
    * so we can run aggregation on them.
    * See [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
@@ -1225,7 +1225,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Groups the [[Dataset]] using the specified columns, so that we can run aggregation on them.
+   * Groups the Dataset using the specified columns, so that we can run aggregation on them.
    * See [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
    * This is a variant of groupBy that can only group by existing columns using column names
@@ -1254,7 +1254,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Scala-specific)
-   * Reduces the elements of this [[Dataset]] using the specified binary function. The given `func`
+   * Reduces the elements of this Dataset using the specified binary function. The given `func`
    * must be commutative and associative or the result may be non-deterministic.
    *
    * @group action
@@ -1310,7 +1310,7 @@ class Dataset[T] private[sql](
     groupByKey(func.call(_))(encoder)
 
   /**
-   * Create a multi-dimensional rollup for the current [[Dataset]] using the specified columns,
+   * Create a multi-dimensional rollup for the current Dataset using the specified columns,
    * so we can run aggregation on them.
    * See [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
@@ -1339,7 +1339,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Create a multi-dimensional cube for the current [[Dataset]] using the specified columns,
+   * Create a multi-dimensional cube for the current Dataset using the specified columns,
    * so we can run aggregation on them.
    * See [[RelationalGroupedDataset]] for all the available aggregate functions.
    *
@@ -1367,7 +1367,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * (Scala-specific) Aggregates on the entire [[Dataset]] without groups.
+   * (Scala-specific) Aggregates on the entire Dataset without groups.
    * {{{
    *   // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
    *   ds.agg("age" -> "max", "salary" -> "avg")
@@ -1382,7 +1382,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * (Scala-specific) Aggregates on the entire [[Dataset]] without groups.
+   * (Scala-specific) Aggregates on the entire Dataset without groups.
    * {{{
    *   // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
    *   ds.agg(Map("age" -> "max", "salary" -> "avg"))
@@ -1395,7 +1395,7 @@ class Dataset[T] private[sql](
   def agg(exprs: Map[String, String]): DataFrame = groupBy().agg(exprs)
 
   /**
-   * (Java-specific) Aggregates on the entire [[Dataset]] without groups.
+   * (Java-specific) Aggregates on the entire Dataset without groups.
    * {{{
    *   // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
    *   ds.agg(Map("age" -> "max", "salary" -> "avg"))
@@ -1408,7 +1408,7 @@ class Dataset[T] private[sql](
   def agg(exprs: java.util.Map[String, String]): DataFrame = groupBy().agg(exprs)
 
   /**
-   * Aggregates on the entire [[Dataset]] without groups.
+   * Aggregates on the entire Dataset without groups.
    * {{{
    *   // ds.agg(...) is a shorthand for ds.groupBy().agg(...)
    *   ds.agg(max($"age"), avg($"salary"))
@@ -1422,9 +1422,9 @@ class Dataset[T] private[sql](
   def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs : _*)
 
   /**
-   * Returns a new [[Dataset]] by taking the first `n` rows. The difference between this function
+   * Returns a new Dataset by taking the first `n` rows. The difference between this function
    * and `head` is that `head` is an action and returns an array (by triggering query execution)
-   * while `limit` returns a new [[Dataset]].
+   * while `limit` returns a new Dataset.
    *
    * @group typedrel
    * @since 2.0.0
@@ -1434,7 +1434,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] containing union of rows in this Dataset and another Dataset.
+   * Returns a new Dataset containing union of rows in this Dataset and another Dataset.
    * This is equivalent to `UNION ALL` in SQL.
    *
    * To do a SQL-style set union (that does deduplication of elements), use this function followed
@@ -1447,7 +1447,7 @@ class Dataset[T] private[sql](
   def unionAll(other: Dataset[T]): Dataset[T] = union(other)
 
   /**
-   * Returns a new [[Dataset]] containing union of rows in this Dataset and another Dataset.
+   * Returns a new Dataset containing union of rows in this Dataset and another Dataset.
    * This is equivalent to `UNION ALL` in SQL.
    *
    * To do a SQL-style set union (that does deduplication of elements), use this function followed
@@ -1463,7 +1463,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] containing rows only in both this Dataset and another Dataset.
+   * Returns a new Dataset containing rows only in both this Dataset and another Dataset.
    * This is equivalent to `INTERSECT` in SQL.
    *
    * Note that, equality checking is performed directly on the encoded representation of the data
@@ -1477,7 +1477,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] containing rows in this Dataset but not in another Dataset.
+   * Returns a new Dataset containing rows in this Dataset but not in another Dataset.
    * This is equivalent to `EXCEPT` in SQL.
    *
    * Note that, equality checking is performed directly on the encoded representation of the data
@@ -1491,7 +1491,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] by sampling a fraction of rows.
+   * Returns a new Dataset by sampling a fraction of rows.
    *
    * @param withReplacement Sample with replacement or not.
    * @param fraction Fraction of rows to generate.
@@ -1505,7 +1505,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] by sampling a fraction of rows, using a random seed.
+   * Returns a new Dataset by sampling a fraction of rows, using a random seed.
    *
    * @param withReplacement Sample with replacement or not.
    * @param fraction Fraction of rows to generate.
@@ -1518,7 +1518,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Randomly splits this [[Dataset]] with the provided weights.
+   * Randomly splits this Dataset with the provided weights.
    *
    * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @param seed Seed for sampling.
@@ -1545,7 +1545,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a Java list that contains randomly split [[Dataset]] with the provided weights.
+   * Returns a Java list that contains randomly split Dataset with the provided weights.
    *
    * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @param seed Seed for sampling.
@@ -1559,7 +1559,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Randomly splits this [[Dataset]] with the provided weights.
+   * Randomly splits this Dataset with the provided weights.
    *
    * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @group typedrel
@@ -1570,7 +1570,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Randomly splits this [[Dataset]] with the provided weights. Provided for the Python Api.
+   * Randomly splits this Dataset with the provided weights. Provided for the Python Api.
    *
    * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @param seed Seed for sampling.
@@ -1580,7 +1580,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * (Scala-specific) Returns a new [[Dataset]] where each row has been expanded to zero or more
+   * (Scala-specific) Returns a new Dataset where each row has been expanded to zero or more
    * rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. The columns of
    * the input row are implicitly joined with each row that is output by the function.
    *
@@ -1623,7 +1623,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * (Scala-specific) Returns a new [[Dataset]] where a single column has been expanded to zero
+   * (Scala-specific) Returns a new Dataset where a single column has been expanded to zero
    * or more rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. All
    * columns of the input row are implicitly joined with each value that is output by the function.
    *
@@ -1664,7 +1664,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] by adding a column or replacing the existing column that has
+   * Returns a new Dataset by adding a column or replacing the existing column that has
    * the same name.
    *
    * @group untypedrel
@@ -1689,7 +1689,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] by adding a column with metadata.
+   * Returns a new Dataset by adding a column with metadata.
    */
   private[spark] def withColumn(colName: String, col: Column, metadata: Metadata): DataFrame = {
     val resolver = sparkSession.sessionState.analyzer.resolver
@@ -1710,7 +1710,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with a column renamed.
+   * Returns a new Dataset with a column renamed.
    * This is a no-op if schema doesn't contain existingName.
    *
    * @group untypedrel
@@ -1735,7 +1735,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with a column dropped. This is a no-op if schema doesn't contain
+   * Returns a new Dataset with a column dropped. This is a no-op if schema doesn't contain
    * column name.
    *
    * This method can only be used to drop top level columns. the colName string is treated
@@ -1749,7 +1749,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with columns dropped.
+   * Returns a new Dataset with columns dropped.
    * This is a no-op if schema doesn't contain column name(s).
    *
    * This method can only be used to drop top level columns. the colName string is treated literally
@@ -1773,8 +1773,8 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with a column dropped.
-   * This version of drop accepts a Column rather than a name.
+   * Returns a new Dataset with a column dropped.
+   * This version of drop accepts a [[Column]] rather than a name.
    * This is a no-op if the Dataset doesn't have a column
    * with an equivalent expression.
    *
@@ -1796,7 +1796,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] that contains only the unique rows from this [[Dataset]].
+   * Returns a new Dataset that contains only the unique rows from this Dataset.
    * This is an alias for `distinct`.
    *
    * @group typedrel
@@ -1805,7 +1805,7 @@ class Dataset[T] private[sql](
   def dropDuplicates(): Dataset[T] = dropDuplicates(this.columns)
 
   /**
-   * (Scala-specific) Returns a new [[Dataset]] with duplicate rows removed, considering only
+   * (Scala-specific) Returns a new Dataset with duplicate rows removed, considering only
    * the subset of columns.
    *
    * @group typedrel
@@ -1825,7 +1825,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] with duplicate rows removed, considering only
+   * Returns a new Dataset with duplicate rows removed, considering only
    * the subset of columns.
    *
    * @group typedrel
@@ -1838,7 +1838,7 @@ class Dataset[T] private[sql](
    * If no columns are given, this function computes statistics for all numerical columns.
    *
    * This function is meant for exploratory data analysis, as we make no guarantee about the
-   * backward compatibility of the schema of the resulting [[Dataset]]. If you want to
+   * backward compatibility of the schema of the resulting Dataset. If you want to
    * programmatically compute summary statistics, use the `agg` function instead.
    *
    * {{{
@@ -1937,7 +1937,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Scala-specific)
-   * Returns a new [[Dataset]] that only contains elements where `func` returns `true`.
+   * Returns a new Dataset that only contains elements where `func` returns `true`.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1954,7 +1954,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Java-specific)
-   * Returns a new [[Dataset]] that only contains elements where `func` returns `true`.
+   * Returns a new Dataset that only contains elements where `func` returns `true`.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1971,7 +1971,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Scala-specific)
-   * Returns a new [[Dataset]] that contains the result of applying `func` to each element.
+   * Returns a new Dataset that contains the result of applying `func` to each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1984,7 +1984,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Java-specific)
-   * Returns a new [[Dataset]] that contains the result of applying `func` to each element.
+   * Returns a new Dataset that contains the result of applying `func` to each element.
    *
    * @group typedrel
    * @since 1.6.0
@@ -1998,7 +1998,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Scala-specific)
-   * Returns a new [[Dataset]] that contains the result of applying `func` to each partition.
+   * Returns a new Dataset that contains the result of applying `func` to each partition.
    *
    * @group typedrel
    * @since 1.6.0
@@ -2014,7 +2014,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Java-specific)
-   * Returns a new [[Dataset]] that contains the result of applying `f` to each partition.
+   * Returns a new Dataset that contains the result of applying `f` to each partition.
    *
    * @group typedrel
    * @since 1.6.0
@@ -2043,7 +2043,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Scala-specific)
-   * Returns a new [[Dataset]] by first applying a function to all elements of this [[Dataset]],
+   * Returns a new Dataset by first applying a function to all elements of this Dataset,
    * and then flattening the results.
    *
    * @group typedrel
@@ -2056,7 +2056,7 @@ class Dataset[T] private[sql](
   /**
    * :: Experimental ::
    * (Java-specific)
-   * Returns a new [[Dataset]] by first applying a function to all elements of this [[Dataset]],
+   * Returns a new Dataset by first applying a function to all elements of this Dataset,
    * and then flattening the results.
    *
    * @group typedrel
@@ -2080,7 +2080,7 @@ class Dataset[T] private[sql](
 
   /**
    * (Java-specific)
-   * Runs `func` on each element of this [[Dataset]].
+   * Runs `func` on each element of this Dataset.
    *
    * @group action
    * @since 1.6.0
@@ -2088,7 +2088,7 @@ class Dataset[T] private[sql](
   def foreach(func: ForeachFunction[T]): Unit = foreach(func.call(_))
 
   /**
-   * Applies a function `f` to each partition of this [[Dataset]].
+   * Applies a function `f` to each partition of this Dataset.
    *
    * @group action
    * @since 1.6.0
@@ -2099,7 +2099,7 @@ class Dataset[T] private[sql](
 
   /**
    * (Java-specific)
-   * Runs `func` on each partition of this [[Dataset]].
+   * Runs `func` on each partition of this Dataset.
    *
    * @group action
    * @since 1.6.0
@@ -2108,7 +2108,7 @@ class Dataset[T] private[sql](
     foreachPartition(it => func.call(it.asJava))
 
   /**
-   * Returns the first `n` rows in the [[Dataset]].
+   * Returns the first `n` rows in the Dataset.
    *
    * Running take requires moving data into the application's driver process, and doing so with
    * a very large `n` can crash the driver process with OutOfMemoryError.
@@ -2119,7 +2119,7 @@ class Dataset[T] private[sql](
   def take(n: Int): Array[T] = head(n)
 
   /**
-   * Returns the first `n` rows in the [[Dataset]] as a list.
+   * Returns the first `n` rows in the Dataset as a list.
    *
    * Running take requires moving data into the application's driver process, and doing so with
    * a very large `n` can crash the driver process with OutOfMemoryError.
@@ -2130,7 +2130,7 @@ class Dataset[T] private[sql](
   def takeAsList(n: Int): java.util.List[T] = java.util.Arrays.asList(take(n) : _*)
 
   /**
-   * Returns an array that contains all of [[Row]]s in this [[Dataset]].
+   * Returns an array that contains all of [[Row]]s in this Dataset.
    *
    * Running collect requires moving all the data into the application's driver process, and
    * doing so on a very large dataset can crash the driver process with OutOfMemoryError.
@@ -2143,7 +2143,7 @@ class Dataset[T] private[sql](
   def collect(): Array[T] = collect(needCallback = true)
 
   /**
-   * Returns a Java list that contains all of [[Row]]s in this [[Dataset]].
+   * Returns a Java list that contains all of [[Row]]s in this Dataset.
    *
    * Running collect requires moving all the data into the application's driver process, and
    * doing so on a very large dataset can crash the driver process with OutOfMemoryError.
@@ -2171,9 +2171,9 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Return an iterator that contains all of [[Row]]s in this [[Dataset]].
+   * Return an iterator that contains all of [[Row]]s in this Dataset.
    *
-   * The iterator will consume as much memory as the largest partition in this [[Dataset]].
+   * The iterator will consume as much memory as the largest partition in this Dataset.
    *
    * Note: this results in multiple Spark jobs, and if the input Dataset is the result
    * of a wide transformation (e.g. join with different partitioners), to avoid
@@ -2189,7 +2189,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns the number of rows in the [[Dataset]].
+   * Returns the number of rows in the Dataset.
    * @group action
    * @since 1.6.0
    */
@@ -2198,7 +2198,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] that has exactly `numPartitions` partitions.
+   * Returns a new Dataset that has exactly `numPartitions` partitions.
    *
    * @group typedrel
    * @since 1.6.0
@@ -2208,7 +2208,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] partitioned by the given partitioning expressions into
+   * Returns a new Dataset partitioned by the given partitioning expressions into
    * `numPartitions`. The resulting Dataset is hash partitioned.
    *
    * This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
@@ -2222,7 +2222,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] partitioned by the given partitioning expressions, using
+   * Returns a new Dataset partitioned by the given partitioning expressions, using
    * `spark.sql.shuffle.partitions` as number of partitions.
    * The resulting Dataset is hash partitioned.
    *
@@ -2237,7 +2237,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] that has exactly `numPartitions` partitions.
+   * Returns a new Dataset that has exactly `numPartitions` partitions.
    * Similar to coalesce defined on an [[RDD]], this operation results in a narrow dependency, e.g.
    * if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of
    * the 100 new partitions will claim 10 of the current partitions.
@@ -2250,7 +2250,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns a new [[Dataset]] that contains only the unique rows from this [[Dataset]].
+   * Returns a new Dataset that contains only the unique rows from this Dataset.
    * This is an alias for `dropDuplicates`.
    *
    * Note that, equality checking is performed directly on the encoded representation of the data
@@ -2262,7 +2262,7 @@ class Dataset[T] private[sql](
   def distinct(): Dataset[T] = dropDuplicates()
 
   /**
-   * Persist this [[Dataset]] with the default storage level (`MEMORY_AND_DISK`).
+   * Persist this Dataset with the default storage level (`MEMORY_AND_DISK`).
    *
    * @group basic
    * @since 1.6.0
@@ -2273,7 +2273,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Persist this [[Dataset]] with the default storage level (`MEMORY_AND_DISK`).
+   * Persist this Dataset with the default storage level (`MEMORY_AND_DISK`).
    *
    * @group basic
    * @since 1.6.0
@@ -2281,7 +2281,7 @@ class Dataset[T] private[sql](
   def cache(): this.type = persist()
 
   /**
-   * Persist this [[Dataset]] with the given storage level.
+   * Persist this Dataset with the given storage level.
    * @param newLevel One of: `MEMORY_ONLY`, `MEMORY_AND_DISK`, `MEMORY_ONLY_SER`,
    *                 `MEMORY_AND_DISK_SER`, `DISK_ONLY`, `MEMORY_ONLY_2`,
    *                 `MEMORY_AND_DISK_2`, etc.
@@ -2295,7 +2295,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Mark the [[Dataset]] as non-persistent, and remove all blocks for it from memory and disk.
+   * Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
    *
    * @param blocking Whether to block until all blocks are deleted.
    *
@@ -2308,7 +2308,7 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Mark the [[Dataset]] as non-persistent, and remove all blocks for it from memory and disk.
+   * Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
    *
    * @group basic
    * @since 1.6.0
@@ -2316,7 +2316,7 @@ class Dataset[T] private[sql](
   def unpersist(): this.type = unpersist(blocking = false)
 
   /**
-   * Represents the content of the [[Dataset]] as an [[RDD]] of [[T]].
+   * Represents the content of the Dataset as an [[RDD]] of [[T]].
    *
    * @group basic
    * @since 1.6.0
@@ -2330,21 +2330,21 @@ class Dataset[T] private[sql](
   }
 
   /**
-   * Returns the content of the [[Dataset]] as a [[JavaRDD]] of [[Row]]s.
+   * Returns the content of the Dataset as a [[JavaRDD]] of [[Row]]s.
    * @group basic
    * @since 1.6.0
    */
   def toJavaRDD: JavaRDD[T] = rdd.toJavaRDD()
 
   /**
-   * Returns the content of the [[Dataset]] as a [[JavaRDD]] of [[Row]]s.
+   * Returns the content of the Dataset as a [[JavaRDD]] of [[Row]]s.
    * @group basic
    * @since 1.6.0
    */
   def javaRDD: JavaRDD[T] = toJavaRDD
 
   /**
-   * Registers this [[Dataset]] as a temporary table using the given name. The lifetime of this
+   * Registers this Dataset as a temporary table using the given name. The lifetime of this
    * temporary table is tied to the [[SparkSession]] that was used to create this Dataset.
    *
    * @group basic
@@ -2394,7 +2394,7 @@ class Dataset[T] private[sql](
 
   /**
    * :: Experimental ::
-   * Interface for saving the content of the [[Dataset]] out into external storage or streams.
+   * Interface for saving the content of the Dataset out into external storage or streams.
    *
    * @group basic
    * @since 1.6.0
@@ -2403,7 +2403,7 @@ class Dataset[T] private[sql](
   def write: DataFrameWriter = new DataFrameWriter(toDF())
 
   /**
-   * Returns the content of the [[Dataset]] as a Dataset of JSON strings.
+   * Returns the content of the Dataset as a Dataset of JSON strings.
    * @since 2.0.0
    */
   def toJSON: Dataset[String] = {