[SPARK-34500][DOCS][EXAMPLES] Replace symbol literals with $"" in examples and documents

docs/sql-data-sources-avro.md

@@ -107,9 +107,9 @@ val df = spark
 // 2. Filter by column `favorite_color`;
 // 3. Encode the column `name` in Avro format.
 val output = df
-  .select(from_avro('value, jsonFormatSchema) as 'user)
+  .select(from_avro($"value", jsonFormatSchema) as $"user")
   .where("user.favorite_color == \"red\"")
-  .select(to_avro($"user.name") as 'value)
+  .select(to_avro($"user.name") as $"value")
 
 val query = output
   .writeStream

examples/src/main/scala/org/apache/spark/examples/sql/SimpleTypedAggregator.scala

@@ -31,7 +31,7 @@ object SimpleTypedAggregator {
       .getOrCreate()
 
     import spark.implicits._
-    val ds = spark.range(20).select(('id % 3).as("key"), 'id).as[(Long, Long)]
+    val ds = spark.range(20).select(($"id" % 3).as("key"), $"id").as[(Long, Long)]
     println("input data:")
     ds.show()
 

sql/core/src/main/scala/org/apache/spark/sql/Column.scala

@@ -1108,7 +1108,7 @@ class Column(val expr: Expression) extends Logging {
    * Gives the column an alias.
    * {{{
    *   // Renames colA to colB in select output.
-   *   df.select($"colA".as('colB))
+   *   df.select($"colA".as("colB"))
    * }}}
    *
    * If the current column has metadata associated with it, this metadata will be propagated

sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala

@@ -2307,7 +2307,7 @@ class Dataset[T] private[sql](
    *   case class Book(title: String, words: String)
    *   val ds: Dataset[Book]
    *
-   *   val allWords = ds.select('title, explode(split('words, " ")).as("word"))
+   *   val allWords = ds.select($"title", explode(split($"words", " ")).as("word"))
    *
    *   val bookCountPerWord = allWords.groupBy("word").agg(count_distinct("title"))
    * }}}
@@ -2346,7 +2346,7 @@ class Dataset[T] private[sql](
    * `functions.explode()`:
    *
    * {{{
-   *   ds.select(explode(split('words, " ")).as("word"))
+   *   ds.select(explode(split($"words", " ")).as("word"))
    * }}}
    *
    * or `flatMap()`:

sql/core/src/main/scala/org/apache/spark/sql/expressions/Window.scala

@@ -136,8 +136,8 @@ object Window {
    *   val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
    *     .toDF("id", "category")
    *   val byCategoryOrderedById =
-   *     Window.partitionBy('category).orderBy('id).rowsBetween(Window.currentRow, 1)
-   *   df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
+   *     Window.partitionBy($"category").orderBy($"id").rowsBetween(Window.currentRow, 1)
+   *   df.withColumn("sum", sum($"id") over byCategoryOrderedById).show()
    *
    *   +---+--------+---+
    *   | id|category|sum|
@@ -188,8 +188,8 @@ object Window {
    *   val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
    *     .toDF("id", "category")
    *   val byCategoryOrderedById =
-   *     Window.partitionBy('category).orderBy('id).rangeBetween(Window.currentRow, 1)
-   *   df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
+   *     Window.partitionBy($"category").orderBy($"id").rangeBetween(Window.currentRow, 1)
+   *   df.withColumn("sum", sum($"id") over byCategoryOrderedById).show()
    *
    *   +---+--------+---+
    *   | id|category|sum|

sql/core/src/main/scala/org/apache/spark/sql/expressions/WindowSpec.scala

@@ -100,8 +100,8 @@ class WindowSpec private[sql](
    *   val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
    *     .toDF("id", "category")
    *   val byCategoryOrderedById =
-   *     Window.partitionBy('category).orderBy('id).rowsBetween(Window.currentRow, 1)
-   *   df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
+   *     Window.partitionBy($"category").orderBy($"id").rowsBetween(Window.currentRow, 1)
+   *   df.withColumn("sum", sum($"id") over byCategoryOrderedById).show()
    *
    *   +---+--------+---+
    *   | id|category|sum|
@@ -168,8 +168,8 @@ class WindowSpec private[sql](
    *   val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
    *     .toDF("id", "category")
    *   val byCategoryOrderedById =
-   *     Window.partitionBy('category).orderBy('id).rangeBetween(Window.currentRow, 1)
-   *   df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
+   *     Window.partitionBy($"category").orderBy($"id").rangeBetween(Window.currentRow, 1)
+   *   df.withColumn("sum", sum($"id") over byCategoryOrderedById).show()
    *
    *   +---+--------+---+
    *   | id|category|sum|