abhishekd0907 commented on a change in pull request #29242:
URL: https://github.com/apache/spark/pull/29242#discussion_r460643781
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File path: python/pyspark/sql/dataframe.py
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@@ -674,7 +674,7 @@ def cache(self):
.. note:: The default storage level has changed to `MEMORY_AND_DISK`
to match Scala in 2.0.
"""
self.is_cached = True
- self._jdf.cache()
+ self.persist(StorageLevel.MEMORY_AND_DISK)
Review comment:
Okay you might be right here but I observed a difference in the behavior
of `pySparkDataframe.cache()` and `pySparkDataframe.persist()` because of
differences in Storage Levels. When `cache()` is used, RDDs are spilled to disk
but when `persist()` is used RDDs are persisted in memory.
This is because `MemoryStore#putIteratorAsValues()` is used when `cache()`
is called and `MemoryStore#putIteratorAsBytes()` is used when `persist()` is
called due to difference in storage levels.
My example:
```
df = spark.range(1,4500000000).cache()
df.count()
AND
df = spark.range(1,4500000000).persist()
df.count()
```
I tried this on Spark version 2.4.6. I have attached RDD storage screenshots
here. Correct me if I'm missing something and this is expected.
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