Spark: convert large input to rdd - scala

Question

I read a lot of lines from an iterator and I need to convert them to an RDD.
I have see some answers like do an sc.parallelize(YourIterable.toList) but the toList will raise a memory exception
I have also read post saying that it is again Spark model but I think there should be another solution.
I have two ideas, please tell me which is the best or if you have any other ideas to solve this.
Solution 1: I store those lines 100 000 by 100 000 to an ArrayBuffer then when the iterator is empty I convert the array to an RDD with parallelize
val result = ArrayBuffer[String]()
var counter = 0
var resultRDD:RDD[Array[Option[Any]]] = sc.sparkContext.emptyRDD[Array[Option[Any]]]
while (resultSet.next()) {
//Do some stuff on resultSet
result.append(stuff)
counter = counter + 1
if(counter % 100000 == 0){
val tmp = sc.sparkContext.parallelize(result)
tmp.count // Need to run an action because result will be cleared
resultRDD = resultRDD union sc.sparkContext.parallelize(result)
result.clear
}
}
// Same for last lines
//use resultRDD
With this method the use of count to force an action on the lazy union before the arrayBuffer.clear is a bit annoying.
Solution 2: Same bunch reads but write in some files in HDFS and next do a sc.textFiles