你可以使用spark中的窗口函数来实现这个功能，它会帮助你对数据进行排序，并获得前一行的值，这将起到作用，我认为最好在代码中添加注解来解释它：

// create the dataframe from your example:
import spark.implicits._
val df = Seq(
  ("2019-07-01 10:01:19.000", "2019-07-01 10:11:00.000"),
  ("2019-07-01 10:10:05.000", "2019-07-01 10:40:00.000"),
  ("2019-07-01 10:35:00.000", "2019-07-01 12:30:00.000"),
  ("2019-07-01 15:20:00.000", "2019-07-01 15:50:00.000"),
  ("2019-07-01 16:10:00.000", "2019-07-01 16:35:00.000"),
  ("2019-07-01 16:30:00.000", "2019-07-01 17:00:00.000"),
).toDF("start", "end")

// A window to sort date by start ascending then end ascending, to get the end of the previous row to check if there's an intersection
val w = Window.orderBy("start", "end")

// transform column from string type to timestamp type
df.select(to_timestamp(col("start")).as("start"), to_timestamp(col("end")).as("end"))
  // prev_end column contains the value of the end column of the previous row
  .withColumn("prev_end", lag("end", 1, null).over(w))
  // create column intersection with value 0 if there's intersection and 1 otherwhise
  .withColumn("intersection", when(col("prev_end").isNull.or(col("prev_end").geq(col("start")).and(col("prev_end").leq(col("end")))), 0).otherwise(1))
  // The key element to this solution: prefix sum over the window to make sure we have the right values of each group
  .withColumn("group", functions.sum("intersection").over(w.rowsBetween(Window.unboundedPreceding, Window.currentRow)))
  .drop("prev_end", "intersection")
  .show(false)

+-------------------+-------------------+-----+
|start              |end                |group|
+-------------------+-------------------+-----+
|2019-07-01 10:01:19|2019-07-01 10:11:00|0    |
|2019-07-01 10:10:05|2019-07-01 10:40:00|0    |
|2019-07-01 10:35:00|2019-07-01 12:30:00|0    |
|2019-07-01 15:20:00|2019-07-01 15:50:00|1    |
|2019-07-01 16:10:00|2019-07-01 16:35:00|2    |
|2019-07-01 16:30:00|2019-07-01 17:00:00|2    |
+-------------------+-------------------+-----+

希望这个能帮上忙。