[jira] [Created] (SPARK-16921) RDD/DataFrame persist() and cache() should return Python context managers

["Nicholas Chammas (JIRA)" <ji...@apache.org>]

Nicholas Chammas created SPARK-16921:
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             Summary: RDD/DataFrame persist() and cache() should return Python context managers
                 Key: SPARK-16921
                 URL: https://issues.apache.org/jira/browse/SPARK-16921
             Project: Spark
          Issue Type: New Feature
          Components: PySpark, Spark Core, SQL
            Reporter: Nicholas Chammas
            Priority: Minor


[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:

{code}
with open('/path/to/file') as f:
    contents = f.read()
    ...
{code}

Once the program exits the with block, {{f}} is automatically closed.

I think it makes sense to apply this pattern to persisting and unpersisting DataFrames and RDDs. 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:

{code}
labeled_data.persist()
model = pipeline.fit(labeled_data)
labeled_data.unpersist()
{code}

If {{persist()}} returned a context manager, you could rewrite this as follows:

{code}
with labeled_data.persist():
    model = pipeline.fit(labeled_data)
{code}

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\_\_()}}



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