Details
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Bug
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Status: Resolved
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Major
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Resolution: Fixed
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None
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None
Description
Spark's Partition and RDD.partitions APIs have a contract which requires custom implementations of RDD.partitions to ensure that for all x, rdd.partitions(x).index == x; in other words, the index reported by a repartition needs to match its position in the partitions array.
If a custom RDD implementation violates this contract, then Spark has the potential to become stuck in an infinite recomputation loop when recomputing a subset of an RDD's partitions, since the tasks that are actually run will not correspond to the missing output partitions that triggered the recomputation. Here's a link to a notebook which demonstrates this problem: https://rawgit.com/JoshRosen/e520fb9a64c1c97ec985/raw/5e8a5aa8d2a18910a1607f0aa4190104adda3424/Violating%2520RDD.partitions%2520contract.html
In order to guard against this infinite loop behavior, I think that Spark should fail-fast and refuse to compute RDDs' whose partitions violate the API contract.
