Github user npanj commented on the pull request:
https://github.com/apache/spark/pull/901#issuecomment-44478613
@rxin - Allowing arbitrary data types sounds like a good idea. I actually
tried to to do something like:
--
def edgeListFile[@specialized(Long, Int, Double) ED: ClassTag](
sc: SparkContext,
path: String,
canonicalOrientation: Boolean = false,
minEdgePartitions: Int = 1)
: Graph[Int, ED] =
and
if (lineArray.length >= 3) lineArray(2).asInstanceOf[ED]
else 1.asInstanceOf[ED]
--
But I am running into this error:
--
java.lang.ClassCastException: java.lang.String cannot be cast to
java.lang.Integer
at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
--
I guess I will have to handle primitive types separately?
(I am quite new to Scala) Are there any known patterns to handle casting?
If you can point me to a code snippet within spark codebase(or else where )
that will be great.
Also do you guys follow any scala code style guide? ; so that I can follow
that for future patches
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