Returns a checkpointed version of this DataFrame. 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.