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https://issues.apache.org/jira/browse/SPARK-1962?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-1962.
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Resolution: Won't Fix
I understand the problem, but referencing counting isn't right semantically
here. (It would also be a significant behavior change.) If a function needs to
operate on a cached RDD, it can cache it, but then needs to know whether to
unpersist when done. Really, it needs to return the persistence level to what
it was before, and there is not just one possibility.
> Add RDD cache reference counting
> --------------------------------
>
> Key: SPARK-1962
> URL: https://issues.apache.org/jira/browse/SPARK-1962
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Affects Versions: 1.0.0
> Reporter: Taeyun Kim
> Priority: Minor
>
> It would be nice if the RDD cache() method incorporate a reference counting
> information.
> That is,
> {code}
> void test()
> {
> JavaRDD<...> rdd = ...;
> rdd.cache(); // to reference count 1. actual caching happens.
> rdd.cache(); // to reference count 2. Nop as long as the storage level
> is the same. Else, exception.
> ...
> rdd.uncache(); // to reference count 1. Nop.
> rdd.uncache(); // to reference count 0. Actual unpersist happens.
> }
> {code}
> This can be useful when writing code in modular way.
> When a function receives an RDD as an argument, it doesn't necessarily know
> the cache status of the RDD.
> But it could want to cache the RDD, since it will use the RDD multiple times.
> But with the current RDD API, it cannot determine whether it should unpersist
> it or leave it alone (so that the caller can continue to use that RDD without
> rebuilding).
> For API compatibility, introducing a new method or adding a parameter may be
> required.
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