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
440 NINTH AVENUE, 11TH FLOOR, NEW YORK, NY 10001
E: daniel.siegm...@velos.io W: www.velos.io
<http://www.velos.io>
