I am curious if there is something similar to sklearn's http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html for apache-spark in the latest 2.0.1 release.
So far I could only find https://spark.apache.org/docs/latest/mllib-statistics.html#stratified-sampling which does not seem to be a great fit for splitting heavily imbalanced dataset into train /test samples.
Spark supports stratified samples as outlined in https://s3.amazonaws.com/sparksummit-share/ml-ams-1.0.1/6-sampling/scala/6-sampling_student.html
df.stat.sampleBy("label", Map(0 -> .10, 1 -> .20, 2 -> .3), 0)