[SPARK-12087][STREAMING] Create new JobConf for every batch in saveAsHadoopFiles

The JobConf object created in `DStream.saveAsHadoopFiles` is used concurrently in multiple places:
* The JobConf is updated by `RDD.saveAsHadoopFile()` before the job is launched
* The JobConf is serialized as part of the DStream checkpoints.
These concurrent accesses (updating in one thread, while the another thread is serializing it) can lead to concurrentModidicationException in the underlying Java hashmap using in the internal Hadoop Configuration object.

The solution is to create a new JobConf in every batch, that is updated by `RDD.saveAsHadoopFile()`, while the checkpointing serializes the original JobConf.

Tests to be added in #9988 will fail reliably without this patch. Keeping this patch really small to make sure that it can be added to previous branches.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #10088 from tdas/SPARK-12087.

(cherry picked from commit 8a75a30)
Signed-off-by: Shixiong Zhu <shixiong@databricks.com>

Author: tdas

streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala

@@ -730,7 +730,8 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
     val serializableConf = new SerializableJobConf(conf)
     val saveFunc = (rdd: RDD[(K, V)], time: Time) => {
       val file = rddToFileName(prefix, suffix, time)
-      rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass, serializableConf.value)
+      rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass,
+        new JobConf(serializableConf.value))
     }
     self.foreachRDD(saveFunc)
   }