doc & refactor: todo scaladoc link (issue hablapps#135)

.github/workflows/release.yml

@@ -19,7 +19,7 @@ jobs:
           PGP_SECRET: ${{ secrets.PGP_SECRET }}
           SONATYPE_PASSWORD: ${{ secrets.SONATYPE_PASSWORD }}
           SONATYPE_USERNAME: ${{ secrets.SONATYPE_USERNAME }}
-      - name: Test & coverage # TODO running tests twice
+      - name: Test & coverage
         run: sbt coverage core/test coverageReport coverageAggregate
       - uses: codecov/codecov-action@v2
         with:

core/src/main/scala/doric/sem/AggregationOps.scala

@@ -55,12 +55,6 @@ private[sem] trait AggregationOps
       * @example {{{
       *   // Compute the average for all numeric columns cubed by department and group.
       *   ds.cube("department".cname, "group".cname).avg()
-      *
-      *   // Compute the max age and average salary, cubed by department and gender.
-      *   ds.cube("department".cname, "gender".cname).agg(Map(
-      *     "salary" -> "avg",
-      *     "age" -> "max"
-      *   ))
       * }}}
       * @see [[doric.doc.DRelationalGroupedDataset]] for all the available aggregate functions.
       * @see [[org.apache.spark.sql.Dataset.cube(col1:* org.apache.spark.sql.Dataset.cube]]
@@ -91,14 +85,7 @@ private[sem] trait AggregationOps
       * @example {{{
       *   // Compute the average for all numeric columns rolled up by department and group.
       *   ds.rollup("department".cname, "group".cname).avg()
-      *
-      *   // Compute the max age and average salary, rolled up by department and gender.
-      *   ds.rollup("department".cname, "gender".cname).agg(Map(
-      *     "salary" -> "avg",
-      *     "age" -> "max"
-      *   ))
       * }}}
-      * @todo this example is not from doric
       * @see [[doric.doc.DRelationalGroupedDataset]] for all the available aggregate functions.
       * @see [[org.apache.spark.sql.Dataset.rollup(col1:* org.apache.spark.sql.Dataset.rollup]]
       * @group Group Dataframe operation

core/src/main/scala/doric/syntax/ArrayColumns.scala

@@ -33,7 +33,8 @@ private[syntax] trait ArrayColumns {
     * Creates a new array column. The input columns must all have the same data type.
     *
     * @group Array Type
-    * @see [[org.apache.spark.sql.functions.array]]
+    * @see org.apache.spark.sql.functions.array
+    * @todo scaladoc link (issue #135)
     */
   def array[T](cols: DoricColumn[T]*): ArrayColumn[T] =
     cols.toList.traverse(_.elem).map(f.array(_: _*)).toDC
@@ -89,7 +90,8 @@ private[syntax] trait ArrayColumns {
       *   the type of the array elements to return.
       * @return
       *   the column reference with the applied transformation.
-      * @see [[org.apache.spark.sql.functions.transform]]
+      * @see org.apache.spark.sql.functions.transform
+      * @todo scaladoc link (issue #135)
       */
     def transform[A](
         fun: DoricColumn[T] => DoricColumn[A]
@@ -110,7 +112,8 @@ private[syntax] trait ArrayColumns {
       *   the type of the elements of the array
       * @return
       *   the column reference with the provided transformation.
-      * @see [[org.apache.spark.sql.functions.transform]]
+      * @see org.apache.spark.sql.functions.transform
+      * @todo scaladoc link (issue #135)
       */
     def transformWithIndex[A](
         fun: (DoricColumn[T], IntegerColumn) => DoricColumn[A]
@@ -136,7 +139,8 @@ private[syntax] trait ArrayColumns {
       *   type of the final value to return
       * @return
       *   the column reference with the applied transformation.
-      * @see [[org.apache.spark.sql.functions.aggregate]]
+      * @see org.apache.spark.sql.functions.aggregate
+      * @todo scaladoc link (issue #135)
       */
     def aggregateWT[A, B](zero: DoricColumn[A])(
         merge: (DoricColumn[A], DoricColumn[T]) => DoricColumn[A],
@@ -160,7 +164,8 @@ private[syntax] trait ArrayColumns {
       *   type of the transformed values.
       * @return
       *   the column reference with the applied transformation.
-      * @see [[org.apache.spark.sql.functions.aggregate]]
+      * @see org.apache.spark.sql.functions.aggregate
+      * @todo scaladoc link (issue #135)
       */
     def aggregate[A](
         zero: DoricColumn[A]
@@ -179,7 +184,8 @@ private[syntax] trait ArrayColumns {
       *   the condition to filter.
       * @return
       *   the column reference with the filter applied.
-      * @see [[org.apache.spark.sql.functions.filter]]
+      * @see org.apache.spark.sql.functions.filter
+      * @todo scaladoc link (issue #135)
       */
     def filter(p: DoricColumn[T] => BooleanColumn): DoricColumn[F[T]] =
       (col.elem, p(x).elem)
@@ -196,7 +202,8 @@ private[syntax] trait ArrayColumns {
       *   (col, index) => predicate, the Boolean predicate to filter the input column
       *   given the index. Indices start at 0.
       * @group Array Type
-      * @see [[org.apache.spark.sql.functions.filter]]
+      * @see org.apache.spark.sql.functions.filter
+      * @todo scaladoc link (issue #135)
       */
     def filterWIndex(
         function: (DoricColumn[T], IntegerColumn) => BooleanColumn
@@ -252,7 +259,8 @@ private[syntax] trait ArrayColumns {
       * `nullReplacement`.
       *
       * @group Array Type
-      * @see [[org.apache.spark.sql.functions.array_join]]
+      * @see org.apache.spark.sql.functions.array_join
+      * @todo scaladoc link (issue #135)
       */
     def join(
         delimiter: StringColumn,
@@ -268,7 +276,8 @@ private[syntax] trait ArrayColumns {
       * Concatenates the elements of `column` using the `delimiter`. Nulls are deleted
       *
       * @group Array Type
-      * @see [[org.apache.spark.sql.functions.array_join]]
+      * @see org.apache.spark.sql.functions.array_join
+      * @todo scaladoc link (issue #135)
       */
     def join(delimiter: StringColumn): StringColumn =
       (col.elem, delimiter.elem)

core/src/main/scala/doric/syntax/CommonColumns.scala

@@ -211,7 +211,7 @@ private[syntax] trait CommonColumns extends ColGetters[NamedDoricColumn] {
       * @group All Types
       * @see [[org.apache.spark.sql.functions.array_repeat(left* org.apache.spark.sql.functions.array_repeat]]
       */
-    def repeatArray[B](times: IntegerColumn): ArrayColumn[T] =
+    def repeatArray(times: IntegerColumn): ArrayColumn[T] =
       (column.elem, times.elem)
         .mapN((c1, c2) => {
           new Column(ArrayRepeat(c1.expr, c2.expr))

core/src/test/scala/doric/TypedColumnTest.scala

@@ -34,8 +34,8 @@ trait TypedColumnTest extends Matchers with DatasetComparer {
   ): Unit = {
     import Equalities._
 
-    val eqCond: BooleanColumn = SparkType[T].dataType.typeName match {
-      case "map" =>
+    val eqCond: BooleanColumn = SparkType[T].dataType match {
+      case _: MapType =>
         val compare: (Column => Column) => BooleanColumn = sparkFun =>
           {
             sparkFun(f.col(doricCol.value)) === sparkFun(f.col(sparkCol.value))