Better, the current location: https://issues.apache.org/jira/browse/SPARK-732


On Fri, May 16, 2014 at 1:47 PM, Mark Hamstra <mark@clearstorydata.com> wrote:
https://spark-project.atlassian.net/browse/SPARK-732


On Fri, May 16, 2014 at 9:05 AM, Daniel Siegmann <daniel.siegmann@velos.io> wrote:
I want to use accumulators to keep counts of things like invalid lines found and such, for reporting purposes. Similar to Hadoop counters. This may seem simple, but my case is a bit more complicated. The code which is creating an RDD from a transform is separated from the code which performs the operation on that RDD - or operations (I can't make any assumption as to how many operations will be done on this RDD). There are two issues: (1) I want to retrieve the accumulator value only after it has been computed, and (2) I don't wan to count the same thing twice if the RDD is recomputed.

Here's a simple example, converting strings to integers. Any records which can't be parsed as an integer are dropped, but I want to count how many times that happens:

def numbers(val input: RDD[String]) : RDD[Int] = {
    val invalidRecords = sc.accumulator(0)
    input.flatMap { record =>
        try {
            Seq(record.toInt)
        } catch {
            case NumberFormatException => invalidRecords += 1; Seq()
        }
    }
}


I need some way to know when the result RDD has been computed so I can get the accumulator value and reset it. Or perhaps it would be better to say I need a way to ensure the accumulator value is computed exactly once for a given RDD. Anyone know a way to do this? Or anything I might look into? Or is this something that just isn't supported in Spark?

--
Daniel Siegmann, Software Developer
Velos
Accelerating Machine Learning

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