Github user squito commented on a diff in the pull request: https://github.com/apache/spark/pull/22112#discussion_r213010846 --- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala --- @@ -1918,3 +1980,19 @@ object RDD { new DoubleRDDFunctions(rdd.map(x => num.toDouble(x))) } } + +/** + * The random level of RDD's output (i.e. what `RDD#compute` returns), which indicates how the + * output will diff when Spark reruns the tasks for the RDD. There are 3 random levels, ordered + * by the randomness from low to high: + * 1. IDEMPOTENT: The RDD output is always same (including order) when rerun. --- End diff -- here too, idempotent is the wrong word for this ... deteminstic? partition-ordered? (I guess "ordered" could make it seem like the entire data is ordered ...)
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