Thank you all for the feedback. As Josh suggested, the issue was due to
extending App.
On Wed, Jan 29, 2014 at 5:57 PM, Josh Rosen <rosenvi...@gmail.com> wrote:
> Try removing the "extends App" and write a "main(args: Array[String])"
> method instead. I think that App affects the serialization (there might be
> some threads about this on the old mailing list).
>
>
> On Wed, Jan 29, 2014 at 2:54 PM, 尹绪森 <yinxu...@gmail.com> wrote:
>
>> Could you give some more details? e.g. the context of your code and the
>> exception stack trace.
>>
>> Your code seems weired, do you have already new a SparkContext ? REPL
>> will add some necessary components while application would not.
>> 2014-1-30 AM4:35于 "Michael Diamant" <diamant.mich...@gmail.com>写道：
>>
>> My team recently began writing Spark jobs to be deployed to a Spark
>>> cluster in the form of a jar. Previously, my team interacted with Spark
>>> via the REPL. The job in question works within the REPL, but fails when
>>> executed non-interactively (i.e. packaged as a jar).
>>>
>>> The job code looks similar to:
>>> // imports
>>> object Runner extends App {
>>> val end = new DateTime()
>>> // additional setup
>>> someRdd.filter(f => end.isAfter(f.date)
>>> }
>>>
>>> The point of this example is that a value, end, is defined local to the
>>> driver. Later in the program's execution, the locally defined value, end,
>>> is referenced in the filter predicate of an RDD. When running
>>> non-interactively, a NPE occurs when 'end' is referenced in the filter
>>> predicate. However, running the exact same code via the REPL executes
>>> successfully.
>>>
>>> Spark environment details are:
>>> Spark version: v0.9 using commit SHA
>>> e2ebc3a9d8bca83bf842b134f2f056c1af0ad2be
>>> Scala version: v2.9.3
>>>
>>> I appreciate any help in identifying bugs/mistakes made.
>>>
>>> Thank you,
>>> Michael
>>>
>>
>
