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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