Context managers
<https://docs.python.org/3/reference/datamodel.html#context-managers> are a
natural way to capture closely related setup and teardown code in Python.
For example, they are commonly used when doing file I/O:
with open('/path/to/file') as f:
contents = f.read()
...
Once the program exits the with block, f is automatically closed.
Does it make sense to apply this pattern to persisting and unpersisting
DataFrames and RDDs? I feel like there are many cases when you want to
persist a DataFrame for a specific set of operations and then unpersist it
immediately afterwards.
For example, take model training. Today, you might do something like this:
labeled_data.persist()
model = pipeline.fit(labeled_data)
labeled_data.unpersist()
If persist() returned a context manager, you could rewrite this as follows:
with labeled_data.persist():
model = pipeline.fit(labeled_data)
Upon exiting the with block, labeled_data would automatically be
unpersisted.
This can be done in a backwards-compatible way since persist() would still
return the parent DataFrame or RDD as it does today, but add two methods to
the object: __enter__() and __exit__()
Does this make sense? Is it attractive?
Nick
