Returns a checkpointed version of this Dataset. Checkpointing can be used to truncate the
logical plan of this DataFrame, which is especially useful in iterative algorithms
where the plan may grow exponentially. It will be saved to files inside the checkpoint
directory set with SparkContext.setCheckpointDir().
New in version 2.1.0.
Whether to checkpoint this DataFrame immediately
This API is experimental.