Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/13780#discussion_r67833027
--- 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 --
@rxin I updated the default here in `persist`, to match `cache`.
But actually, it's still not quite correct - the default storage levels for
Python are all serialized. But the MEMORY-based ones don't match the Scala side
(which are deserialized). This was done for RDDs but doesn't quite work for
DataFrames (since DF on the Scala side is cached deserialized by default).
So here `df.cache()` results in `MEMORY_AND_DISK (deser)` while
`df.persist()` results in `MEMORY_AND_DISK (ser)`. Ideally I'd say we don't
want to encourage users to accidentally use the serialized forms for DF caching
(since it is less efficient, as I understand it?). Let me know what you think
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