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From "Josh Rosen (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-13021) Fail fast when custom RDD's violate RDD.partition's API contract
Date Tue, 26 Jan 2016 23:53:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-13021?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Josh Rosen updated SPARK-13021:
-------------------------------
    Target Version/s: 2.0.0  (was: 1.6.1, 2.0.0)

> Fail fast when custom RDD's violate RDD.partition's API contract
> ----------------------------------------------------------------
>
>                 Key: SPARK-13021
>                 URL: https://issues.apache.org/jira/browse/SPARK-13021
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>            Reporter: Josh Rosen
>            Assignee: Josh Rosen
>
> 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.



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