Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/1551#discussion_r15568273
--- Diff: core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
---
@@ -344,7 +345,12 @@ private[spark] object PythonRDD extends Logging {
throw new SparkException("Unexpected Tuple2 element type " +
pair._1.getClass)
}
case other =>
- throw new SparkException("Unexpected element type " +
first.getClass)
+ if (other == null) {
+ dataOut.writeInt(SpecialLengths.NULL)
+ writeIteratorToStream(iter, dataOut)
--- End diff --
If users want to call UDF in Java/Scala from PySpark, they have to use this
private API to do it, so it's possible to have null in RDD[string] or
RDD[Array[Byte]].
BTW, it will be helpful if we can skip some BAD rows during map/reduce,
which was mentioned in MapReduce paper. This is not MUST have feature, but it
really improve the robustness of whole framework, very useful for large scale
jobs.
This PR try to improve the stability of PySpark, let users feel safer and
happier to hack in PySpark.
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