Github user ankurdave commented on the pull request: https://github.com/apache/spark/pull/4234#issuecomment-71752716 I don't think this will always have the desired effect. In the cases where you unpersist upstream RDDs before materializing their results, it's equivalent to never caching anything at all. Rather than ``` val start = System.currentTimeMillis val a = sc.parallelize(Array(1)).map(x => { Thread.sleep(5000); x}).cache() a.count() // Use a once val b = a.map(identity).cache() // Use a again // Problem: b has not been computed yet, so a hasn't been used a.unpersist() b.count() // Now compute b using a System.currentTimeMillis - start // => 10 seconds ``` the correct pattern is to materialize a result (here `b`) before unpersisting its upstream RDDs: ``` val start = System.currentTimeMillis val a = sc.parallelize(Array(1)).map(x => { Thread.sleep(5000); x}).cache() a.count() // Use a once val b = a.map(identity).cache() b.count() // materialize b (cache and compute it), forcing a to be used again a.unpersist() System.currentTimeMillis - start // => 5 seconds ```
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