spark是一个懒惰的框架，需要一个完整的作业（从读到写）来执行，它不能只执行一部分。
要在不重新处理的情况下进行这些更改，可以缓存Dataframe，以尽可能快的速度恢复，类似这样的操作。

val calculatedDF = readSomeDataset()
  .repartition()
  .mapPartition { // do some calculation }
  .cache() // or persist if can't fit in memory of the executors

if (caculatedDF.map(checkEackAreOK).reduce(_ && _).head) { // a condition to see if the calculations are ok and an action to launch it
  println("correct calculation")
  calculatedDF
    .mapPartition { // do some IO }
    .collect()
} else {
  println("incorrect calculation")
}