Github user holdenk commented on a diff in the pull request: https://github.com/apache/spark/pull/13780#discussion_r73432445 --- Diff: python/pyspark/sql/dataframe.py --- @@ -376,24 +376,47 @@ def foreachPartition(self, f): @since(1.3) def cache(self): - """ Persists with the default storage level (C{MEMORY_ONLY}). + """Persists the :class:`DataFrame` with the default storage level (C{MEMORY_AND_DISK}). + + .. note:: the default storage level has changed to C{MEMORY_AND_DISK} to match Scala in 2.0. """ self.is_cached = True self._jdf.cache() return self @since(1.3) - def persist(self, storageLevel=StorageLevel.MEMORY_ONLY): - """Sets the storage level to persist its values across operations - after the first time it is computed. This can only be used to assign - a new storage level if the RDD does not have a storage level set yet. - If no storage level is specified defaults to (C{MEMORY_ONLY}). + def persist(self, storageLevel=StorageLevel.MEMORY_AND_DISK): --- End diff -- One downside of that approach is the user can't easily explicitly cache in-memory only deserialized or even cache on two machines deserialized easily.
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